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url: http://arxiv.org/abs/2310.13149v1
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[2310.13149v1] Understanding Generative AI in Art: An Interview Study with Artists on G-AI from an HCI Perspective

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# Computer Science > Human-Computer Interaction

**arXiv:2310.13149v1** (cs) 

[Submitted on 19 Oct 2023]

# Title:Understanding Generative AI in Art: An Interview Study with Artists on G-AI from an HCI Perspective

Authors:[Jingyu Shi](https://arxiv.org/search/cs?searchtype=author&query=Shi,+J), [Rahul Jain](https://arxiv.org/search/cs?searchtype=author&query=Jain,+R), [Runlin Duan](https://arxiv.org/search/cs?searchtype=author&query=Duan,+R), [Karthik Ramani](https://arxiv.org/search/cs?searchtype=author&query=Ramani,+K)

View a PDF of the paper titled Understanding Generative AI in Art: An Interview Study with Artists on G-AI from an HCI Perspective, by Jingyu Shi and 3 other authors

[View PDF](/pdf/2310.13149v1)

> Abstract:The emergence of Generative Artificial Intelligence (G-AI) has changed the landscape of creative arts with its power to compose novel artwork and thus brought ethical concerns. Despite the efforts by prior works to address these concerns from technical and societal perspectives, there exists little discussion on this topic from an HCI point of view, considering the artists as human factors. We sought to investigate the impact of G-AI on artists, understanding the relationship between artists and G-AI, in order to motivate the underlying HCI research. We conducted semi-structured interviews with artists ($N=25$) from diverse artistic disciplines involved with G-AI in their artistic creation. We found (1) a dilemma among the artists, (2) a disparity in the understanding of G-AI between the artists and the AI developers(3) a tendency to oppose G-AI among the artists. We discuss the future opportunities of HCI research to tackle the problems identified from the interviews. 

Subjects: |  Human-Computer Interaction (cs.HC)  
---|---  
Cite as: | [arXiv:2310.13149](https://arxiv.org/abs/2310.13149) [cs.HC]  
  | (or  [arXiv:2310.13149v1](https://arxiv.org/abs/2310.13149v1) [cs.HC] for this version)   
  |  <https://doi.org/10.48550/arXiv.2310.13149> Focus to learn more arXiv-issued DOI via DataCite  
  
## Submission history

From: Jingyu Shi [[view email](/show-email/9a2e324c/2310.13149)]   
**[v1]** Thu, 19 Oct 2023 20:46:52 UTC (10,674 KB)  


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url: http://arxiv.org/abs/2412.00176v1
title: [2412.00176v1] Art-Free Generative Models: Art Creation Without Graphic Art Knowledge
scrape_date: 2025-03-11T13:08:34.398673
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# Computer Science > Computer Vision and Pattern Recognition

**arXiv:2412.00176v1** (cs) 

[Submitted on 29 Nov 2024]

# Title:Art-Free Generative Models: Art Creation Without Graphic Art Knowledge

Authors:[Hui Ren](https://arxiv.org/search/cs?searchtype=author&query=Ren,+H), [Joanna Materzynska](https://arxiv.org/search/cs?searchtype=author&query=Materzynska,+J), [Rohit Gandikota](https://arxiv.org/search/cs?searchtype=author&query=Gandikota,+R), [David Bau](https://arxiv.org/search/cs?searchtype=author&query=Bau,+D), [Antonio Torralba](https://arxiv.org/search/cs?searchtype=author&query=Torralba,+A)

View a PDF of the paper titled Art-Free Generative Models: Art Creation Without Graphic Art Knowledge, by Hui Ren and 4 other authors

[View PDF](/pdf/2412.00176v1)

> Abstract:We explore the question: "How much prior art knowledge is needed to create art?" To investigate this, we propose a text-to-image generation model trained without access to art-related content. We then introduce a simple yet effective method to learn an art adapter using only a few examples of selected artistic styles. Our experiments show that art generated using our method is perceived by users as comparable to art produced by models trained on large, art-rich datasets. Finally, through data attribution techniques, we illustrate how examples from both artistic and non-artistic datasets contributed to the creation of new artistic styles. 

Subjects: |  Computer Vision and Pattern Recognition (cs.CV)  
---|---  
Cite as: | [arXiv:2412.00176](https://arxiv.org/abs/2412.00176) [cs.CV]  
  | (or  [arXiv:2412.00176v1](https://arxiv.org/abs/2412.00176v1) [cs.CV] for this version)   
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## Submission history

From: Joanna Materzynska [[view email](/show-email/923ab930/2412.00176)]   
**[v1]** Fri, 29 Nov 2024 18:59:01 UTC (44,372 KB)  


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url: http://arxiv.org/abs/2401.05790v1
title: [2401.05790v1] Development in times of hype: How freelancers explore Generative AI?
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# Computer Science > Software Engineering

**arXiv:2401.05790v1** (cs) 

[Submitted on 11 Jan 2024]

# Title:Development in times of hype: How freelancers explore Generative AI?

Authors:[Mateusz Dolata](https://arxiv.org/search/cs?searchtype=author&query=Dolata,+M), [Norbert Lange](https://arxiv.org/search/cs?searchtype=author&query=Lange,+N), [Gerhard Schwabe](https://arxiv.org/search/cs?searchtype=author&query=Schwabe,+G)

View a PDF of the paper titled Development in times of hype: How freelancers explore Generative AI?, by Mateusz Dolata and 2 other authors

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> Abstract:The rise of generative AI has led many companies to hire freelancers to harness its potential. However, this technology presents unique challenges to developers who have not previously engaged with it. Freelancers may find these challenges daunting due to the absence of organizational support and their reliance on positive client feedback. In a study involving 52 freelance developers, we identified multiple challenges associated with developing solutions based on generative AI. Freelancers often struggle with aspects they perceive as unique to generative AI such as unpredictability of its output, the occurrence of hallucinations, and the inconsistent effort required due to trial-and-error prompting cycles. Further, the limitations of specific frameworks, such as token limits and long response times, add to the complexity. Hype-related issues, such as inflated client expectations and a rapidly evolving technological ecosystem, further exacerbate the difficulties. To address these issues, we propose Software Engineering for Generative AI (SE4GenAI) and Hype-Induced Software Engineering (HypeSE) as areas where the software engineering community can provide effective guidance. This support is essential for freelancers working with generative AI and other emerging technologies. 

Subjects: |  Software Engineering (cs.SE)  
---|---  
Cite as: | [arXiv:2401.05790](https://arxiv.org/abs/2401.05790) [cs.SE]  
  | (or  [arXiv:2401.05790v1](https://arxiv.org/abs/2401.05790v1) [cs.SE] for this version)   
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Journal reference: | ICSE (International Conference on Software Engineering, ACM/IEEE), 2024  
Related DOI:  | <https://doi.org/10.1145/3597503.3639111> Focus to learn more DOI(s) linking to related resources   
  
## Submission history

From: Mateusz Dolata [[view email](/show-email/e2da7e9c/2401.05790)]   
**[v1]** Thu, 11 Jan 2024 09:49:50 UTC (1,520 KB)  


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url: http://arxiv.org/abs/2209.02388v2
title: [2209.02388v2] Pathway to Future Symbiotic Creativity
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# Computer Science > Artificial Intelligence

**arXiv:2209.02388v2** (cs) 

[Submitted on 18 Aug 2022 ([v1](https://arxiv.org/abs/2209.02388v1)), last revised 13 Sep 2023 (this version, v2)]

# Title:Pathway to Future Symbiotic Creativity

Authors:[Yike Guo](https://arxiv.org/search/cs?searchtype=author&query=Guo,+Y), [Qifeng Liu](https://arxiv.org/search/cs?searchtype=author&query=Liu,+Q), [Jie Chen](https://arxiv.org/search/cs?searchtype=author&query=Chen,+J), [Wei Xue](https://arxiv.org/search/cs?searchtype=author&query=Xue,+W), [Jie Fu](https://arxiv.org/search/cs?searchtype=author&query=Fu,+J), [Henrik Jensen](https://arxiv.org/search/cs?searchtype=author&query=Jensen,+H), [Fernando Rosas](https://arxiv.org/search/cs?searchtype=author&query=Rosas,+F), [Jeffrey Shaw](https://arxiv.org/search/cs?searchtype=author&query=Shaw,+J), [Xing Wu](https://arxiv.org/search/cs?searchtype=author&query=Wu,+X), [Jiji Zhang](https://arxiv.org/search/cs?searchtype=author&query=Zhang,+J), [Jianliang Xu](https://arxiv.org/search/cs?searchtype=author&query=Xu,+J)

View a PDF of the paper titled Pathway to Future Symbiotic Creativity, by Yike Guo and 10 other authors

[View PDF](/pdf/2209.02388v2)

> Abstract:This report presents a comprehensive view of our vision on the development path of the human-machine symbiotic art creation. We propose a classification of the creative system with a hierarchy of 5 classes, showing the pathway of creativity evolving from a mimic-human artist (Turing Artists) to a Machine artist in its own right. We begin with an overview of the limitations of the Turing Artists then focus on the top two-level systems, Machine Artists, emphasizing machine-human communication in art creation. In art creation, it is necessary for machines to understand humans' mental states, including desires, appreciation, and emotions, humans also need to understand machines' creative capabilities and limitations. The rapid development of immersive environment and further evolution into the new concept of metaverse enable symbiotic art creation through unprecedented flexibility of bi-directional communication between artists and art manifestation environments. By examining the latest sensor and XR technologies, we illustrate the novel way for art data collection to constitute the base of a new form of human-machine bidirectional communication and understanding in art creation. Based on such communication and understanding mechanisms, we propose a novel framework for building future Machine artists, which comes with the philosophy that a human-compatible AI system should be based on the "human-in-the-loop" principle rather than the traditional "end-to-end" dogma. By proposing a new form of inverse reinforcement learning model, we outline the platform design of machine artists, demonstrate its functions and showcase some examples of technologies we have developed. We also provide a systematic exposition of the ecosystem for AI-based symbiotic art form and community with an economic model built on NFT technology. Ethical issues for the development of machine artists are also discussed. 

Subjects: |  Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)  
---|---  
Cite as: | [arXiv:2209.02388](https://arxiv.org/abs/2209.02388) [cs.AI]  
  | (or  [arXiv:2209.02388v2](https://arxiv.org/abs/2209.02388v2) [cs.AI] for this version)   
  |  <https://doi.org/10.48550/arXiv.2209.02388> Focus to learn more arXiv-issued DOI via DataCite  
  
## Submission history

From: Wei Xue [[view email](/show-email/d214c430/2209.02388)]   
**[[v1]](/abs/2209.02388v1)** Thu, 18 Aug 2022 15:12:02 UTC (4,270 KB)  
**[v2]** Wed, 13 Sep 2023 08:37:39 UTC (7,532 KB)  


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url: http://arxiv.org/abs/2403.15262v2
title: [2403.15262v2] Strategic Responses to Technological Change: Evidence from ChatGPT and Upwork
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# Economics > General Economics

**arXiv:2403.15262v2** (econ) 

[Submitted on 22 Mar 2024 ([v1](https://arxiv.org/abs/2403.15262v1)), last revised 24 Apr 2024 (this version, v2)]

# Title:Strategic Responses to Technological Change: Evidence from ChatGPT and Upwork

Authors:[Shun Yiu](https://arxiv.org/search/econ?searchtype=author&query=Yiu,+S), [Rob Seamans](https://arxiv.org/search/econ?searchtype=author&query=Seamans,+R), [Manav Raj](https://arxiv.org/search/econ?searchtype=author&query=Raj,+M), [Ted Liu](https://arxiv.org/search/econ?searchtype=author&query=Liu,+T)

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> Abstract:AI technologies have the potential to affect labor market outcomes by both increasing worker productivity and reducing the demand for certain skills or tasks. Such changes may have important implications for the ways in which workers seek jobs and position themselves. In this project, we examine how freelancers change their strategic behavior on an online work platform following the launch of ChatGPT in December 2022. We present evidence suggesting that, in response to technological change, freelancers increase the concentration of job applications across specializations and differentiate themselves from both their past behavior and their peers. We show that such changes are shaped by the extent to which a freelancer experiences growth in demand or supply in the domains they specialize in. We document heterogeneity in this effect across freelancer characteristics and document how strategic repositioning can help mitigate the negative effects of technological change on freelancer performance on the platform. 

Subjects: |  General Economics (econ.GN)  
---|---  
Cite as: | [arXiv:2403.15262](https://arxiv.org/abs/2403.15262) [econ.GN]  
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**[[v1]](/abs/2403.15262v1)** Fri, 22 Mar 2024 15:00:42 UTC (513 KB)  
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url: http://arxiv.org/abs/2501.12374v1
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[2501.12374v1] Expertise elevates AI usage: experimental evidence comparing laypeople and professional artists

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# Computer Science > Human-Computer Interaction

**arXiv:2501.12374v1** (cs) 

[Submitted on 21 Jan 2025]

# Title:Expertise elevates AI usage: experimental evidence comparing laypeople and professional artists

Authors:[Thomas F. Eisenmann](https://arxiv.org/search/cs?searchtype=author&query=Eisenmann,+T+F), [Andres Karjus](https://arxiv.org/search/cs?searchtype=author&query=Karjus,+A), [Mar Canet Sola](https://arxiv.org/search/cs?searchtype=author&query=Sola,+M+C), [Levin Brinkmann](https://arxiv.org/search/cs?searchtype=author&query=Brinkmann,+L), [Bramantyo Ibrahim Supriyatno](https://arxiv.org/search/cs?searchtype=author&query=Supriyatno,+B+I), [Iyad Rahwan](https://arxiv.org/search/cs?searchtype=author&query=Rahwan,+I)

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> Abstract:Novel capacities of generative AI to analyze and generate cultural artifacts raise inevitable questions about the nature and value of artistic education and human expertise. Has AI already leveled the playing field between professional artists and laypeople, or do trained artistic expressive capacity, curation skills and experience instead enhance the ability to use these new tools? In this pre-registered study, we conduct experimental comparisons between 50 active artists and a demographically matched sample of laypeople. We designed two tasks to approximate artistic practice for testing their capabilities in both faithful and creative image creation: replicating a reference image, and moving as far away as possible from it. We developed a bespoke platform where participants used a modern text-to-image model to complete both tasks. We also collected and compared participants' sentiments towards AI. On average, artists produced more faithful and creative outputs than their lay counterparts, although only by a small margin. While AI may ease content creation, professional expertise is still valuable - even within the confined space of generative AI itself. Finally, we also explored how well an exemplary vision-capable large language model (GPT-4o) would complete the same tasks, if given the role of an image generation agent, and found it performed on par in copying but outperformed even artists in the creative task. The very best results were still produced by humans in both tasks. These outcomes highlight the importance of integrating artistic skills with AI training to prepare artists and other visual professionals for a technologically evolving landscape. We see a potential in collaborative synergy with generative AI, which could reshape creative industries and education in the arts. 

Comments: | Eisenmann and Karjus contributed equally to this work and share first authorship  
---|---  
Subjects: |  Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)  
Cite as: | [arXiv:2501.12374](https://arxiv.org/abs/2501.12374) [cs.HC]  
  | (or  [arXiv:2501.12374v1](https://arxiv.org/abs/2501.12374v1) [cs.HC] for this version)   
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## Submission history

From: Andres Karjus [[view email](/show-email/2210bf41/2501.12374)]   
**[v1]** Tue, 21 Jan 2025 18:53:21 UTC (19,137 KB)  


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url: https://www.corralldesign.com/writing/ai-harm-hypocrisy
title: The harm & hypocrisy of AI art — Matt Corrall
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# The harm & hypocrisy of AI art

13 Jan

Written By [Matt Corrall](/writing?author=5bcf3a585ce350691198d8e5)

> “How easy it is to create and maintain the illusion of understanding, hence perhaps of judgement deserving of credibility... A certain danger lurks there.”

-- Joseph Weizenabum

_(Disclaimer: The views expressed here are my own personal opinions, and not those of Sony Interactive Entertainment)_

For anyone who works in tech, generative artificial intelligence (AI) has been the hot topic of the last couple of years. We’ve seen an explosion of interest around the tech world, with companies big and small scrambling to adopt AI tools and weave AI features into their software. Everyone is seemingly terrified of missing out on what we’re told is the next big thing; even Photoshop - the old reliable friend of graphic design - includes AI tools, now. Stories appear daily about how AI will change society and propel us all into a brighter future, their writers proclaiming a raft of utopian assertions from the end of poverty to making everyone an artist.

I’ve started to see AI image generators and other tools popping up with startling frequency in the daily work and discussions I have with my peers. Engineers ask [ChatGPT ](https://chat.openai.com/auth/login)to write code, managers use [Midjourney](https://www.midjourney.com/home?callbackUrl=%2Fexplore) to fill their PowerPoints with bespoke images, and even designers ask [Dall-E](https://openai.com/research/dall-e) to create UI concepts for them. Nearly everyone seems awed by the novelty and magic of this new toy, and I often hear the same mantra - everyone’s job will soon rely on this. Embrace it, or risk being left behind.

![](https://images.squarespace-cdn.com/content/v1/63b16acfbd1865531d7c3949/142ca062-6521-47ae-98d7-7247b73854ad/1_CiC4C7bodsoTkLq_ap2SHg.png)

UI mockups auto-generated by Midjourney. Source: https://t.ly/B6HlH

So I started digging into both how AI works, and what impact it’s having, particularly on the creative industry I’m proud to be a part of. This article has been a long time coming for me. The more I’ve learned, the more I’ve become first alarmed, then angry, then compelled to speak out. As a designer in the tech world, I want to put forward a different view to one we usually hear, and to explain clearly why I for one, am appalled by the arrival of AI.

#### **AI in 2024**

Machine or deep learning models - that which we now brand AI - have been around in a basic form since the 1950s, but thanks to recent advancements in GPUs, have made a huge leap forward in capacity. What separates AI from say, the cloudy metaverse visions of last year, is that AI is already shipping and impacting our lives for the worse - even if it’s not quite the stuff of everyday pub debate.

Using generative AI tools like [ChatGPT](https://chat.openai.com/auth/login), and [Stable Diffusion](https://stability.ai/news/stable-diffusion-public-release), people can type a request into a browser in straightforward English, and get back a whatever they asked for - be that a recipe, an illustration, or even a faked photo or video. With gadgets like the [Humane AI Pin](https://hu.ma.ne/aipin), people can also do the same by speaking out loud, in a manner reminiscent of the sci-fi movie, [Her](https://www.imdb.com/title/tt1798709/).

![](https://images.squarespace-cdn.com/content/v1/63b16acfbd1865531d7c3949/c7a394c8-5f4f-4a48-b126-e02fa88f0ac8/hero.jpg)

The Humane AI Pin. Source: https://shorturl.at/qWZ58

When using an AI generator the images and articles that come back are often uncanny and strange, but still strikingly realistic - good enough to pass for something written, painted or photographed by a real person. Yet it’s in the details where they still fall over - at least for now - showing hands with seven fingers, writing repetitive prose, or drawing what should be text as gibberish symbols. Despite the limitations, the appeal of cheap, instantly available art and writing holds obvious appeal for the commercial world, and AI companies have quickly amassed colossal profits. [OpenAI](https://openai.com/) for example, making an incredible [$1.6 billion](https://www.theinformation.com/articles/openais-annualized-revenue-tops-1-6-billion-as-customers-shrug-off-ceo-drama) last year.

#### **How it works**

What we call AI today is nothing like the sentient robots of sci-fi movies - an ‘AI’ is a mathematical model, written in computer code, that is good at spotting patterns and correlations in data. In most cases, ‘data’ means a big pile of text or images - millions of photos or online articles, for example. AI companies can feed their models huge libraries of photos or books, and by analysing everything it’s fed, the model can then generate its own versions which look pretty close to the real thing. At first the model is likely to produce complete garbage, but through a trial and error process can be ‘trained’ by an engineer to generate some impressive results.

![](https://images.squarespace-cdn.com/content/v1/63b16acfbd1865531d7c3949/cb05d0bc-1c53-4978-a558-531fb851f119/Stability%2BAI%2BStable%2BDiffusion%2BArt.jpg)

The sample image Stable Diffusion currently use to introduce their AI tool. Source: https://shorturl.at/kuvFH

Training a model requires a vast amount of ‘data,’ and also comes at a not insignificant cost in terms or energy, carbon emissions and human labour. All that ‘data’ has so far mostly been scraped from the internet - taken in secret from people who didn’t know and didn’t consent to handing it over.

Low-paid workers are used to manually filter offensive and upsetting images out of the datasets, and to type out description tags for the millions of images, so that the machine knows what its looking at. Programmes like Amazon’s [Mechanical Turk](https://www.mturk.com) \- which pays a pittance to people for each image they tag - hides struggling workers behind a curtain whilst giving the impression of a shiny, automated front-end.

![](https://images.squarespace-cdn.com/content/v1/63b16acfbd1865531d7c3949/2293d893-a57f-4f6c-92f5-fab317550a3e/1538492931-20181002.png)

Source: https://www.smbc-comics.com/comic/rise-of-the-machines

#### **What AI can and can’t do**

Everything an AI is fed must first be categorised and converted to numerical data, in order to be consumed. There is a process of abstraction and reduction that must happen, turning a digital image into a numerical matrix of RGB pixel colours and positions. This abstraction means a whole lot of nuance, detail and context that we understand as humans - anything that can’t be easily quantified - is lost as data is fed into the machine. The Mona Lisa becomes just more numbers in the pile. 

AI will also amplify any inherent bias in its training dataset. For example, if a model is trained on a police database and told to suggest jail sentences in a courtroom - [something which is already happening in the US](https://www.wired.com/2017/04/courts-using-ai-sentence-criminals-must-stop-now/) \- it will have inherited any racial bias that was present in the records of arrests, and be more likely for example, to suggest harsher sentences for black defendants. Famously too, if one prompts Dall-E with the word ‘CEO,’ it will only produce images of white men.

AI models are highly dependent on the input dataset - what it contains, what’s conspicuously absent, who curated it and to what end, all affect the results. Without any governance whatsoever, that means Silicon Valley tech companies get to decide exactly which way the models lean and what they do - and the more widely AI is used, the more their particular world view and biases propagate.

![](https://images.squarespace-cdn.com/content/v1/63b16acfbd1865531d7c3949/3e1e5e79-c0be-4bfc-8bed-cf4f7262690f/artificial-intelligence-modeling.jpg)

The process of training an AI model on vast quantities of input data  
Source: https://forbytes.com/blog/ai-models-explained

When someone asks the machine to generate an image, the model scans over everything in its dataset and makes a calculation - out of everything it has, which combination of colours, shapes and lines best fits the request? It’s a mathematical brute force approach, not unlike the classic adage of infinite monkeys at typewriters - feed the AI enough data and it will eventually give you something about right. The machine doesn’t draw or paint - it can only break down its dataset into billions of parts, and recombine them to deliver mash-ups of what came before, as strange and uncanny as they may be.

In this way, an AI model is technically incapable of producing anything new. It’s limited view of the world is based entirely on the abstracted number set it was given. An AI model amazes only because it has devoured so much raw material, to remix at your behest. As clever as it may be at spotting patterns, it cannot adapt, interpret or imagine like a human being can. What it can do very well however, is copy an artists’ style or fake a photo with disturbing accuracy.

![](https://images.squarespace-cdn.com/content/v1/63b16acfbd1865531d7c3949/c69410e4-21cc-4772-bd95-1338a2f0b171/bba27e83-602e-4b38-a7f2-8b556ad82193_1024x499.jpg)

Illustrations by artist [Hollie Mengert](https://holliemengert.com) (left) & AI output based on her stolen work (right)  
Source: https://thealgorithmicbridge.substack.com/p/why-generative-ai-angers-artists

![](https://images.squarespace-cdn.com/content/v1/63b16acfbd1865531d7c3949/32317294-2e31-4574-a988-8038d97e6fcf/fakephotos.jpg)

Entirely AI-generated fake photos - alarmingly difficult to tell from the real thing

Part of the branding exercise around AI tools has been about building mystique. AI enthusiasts talk about its limitless potential to transform society for the better, by taking over more and more decisions for us. There is an almost religious zeal for this in some quarters, from which predictions flow of utopian futures where all problems are solved through learning to embrace the wisdom that flows from these machines; but as we’re seeing, these machines are far cruder and more flawed than the hype suggests.

> “Generative AI is the key to solving some of the world’s biggest problems, such as climate change, poverty, and disease. It has the potential to make the world a better place for everyone”

-- Meta CEO, Mark Zuckerberg

One aspect of AI’s working which perhaps fuels these beliefs, is the opaqueness of its decisions. As models are trained, they make myriad correlations and connections which are never fully visible to the engineers. Whilst not truly a ‘black box,’ models nevertheless produce output without us fully knowing what’s going on inside. This lack of accountability means it would be unforgivable to say, trust them with decisions that affect people’s lives. By the same token, the box of mystery can appear as if magical - the machine really looks like it’s thinking and understanding, prompting some to proclaim that in time anything is possible. AI is not intelligent, however. It blindly connects and calculates, no matter how misguided or illicit the task.

![](https://images.squarespace-cdn.com/content/v1/63b16acfbd1865531d7c3949/11b61744-9ddf-4342-834c-f7311ecd952d/24775b2eef9f7bc0b7d4a2950c1e82f2.png)

For now, AI-generated images can be spotted via the mistakes they make with fine details - such as human fingers - but we can expect this to change  
Source: https://t.ly/HPduD

> “Generative A.I. art is vampirical - feasting on past generations of artwork even as it sucks the lifeblood from living artists. Over time, this will impoverish our visual culture.”

-- Molly Crabapple

#### **The Vampire Machine**

It’s important to understand that [OpenAI](https://openai.com) (creator of Dalle-E), [StabilityAI](https://stability.ai) (creator of Stable Diffusion) and [Midjourney](https://www.midjourney.com/home?callbackUrl=%2Fexplore) \- the big three image generators - all trained their successful models by scraping millions of other peoples’ images from the internet - apparently entirely without the owners’ knowledge or permission. Lawsuits surrounding this are still ongoing.

This is worth reiterating: The billion-dollar-making generators we see today appear trained on the copyrighted works of far poorer artists, illustrators and photographers; taken directly from their portfolios and community sites like [DeviantArt.](https://www.deviantart.com) This is copyright infringement on a completely unprecedented scale, and in my opinion corrupt, cynical and immoral. Paying users can even directly prompt the image generators to produce artwork in the style of an artist by typing their name - making no secret of the fact that their work was absorbed by the model.

![](https://images.squarespace-cdn.com/content/v1/63b16acfbd1865531d7c3949/35c319e7-8015-46b4-9288-b5e651e07dea/gregrutkowski.jpg)

‘Dragon Cage’ by artist Greg Rutkowski (top), whose name has been used tens of thousands of times as a prompt in Stable Diffusion to generate lookalike images (below).  
Source: https://www.businessinsider.com/ai-image-generators-artists-copying-style-thousands-images-2022-10?r=US&IR=T

Only because this has never happened at such scale and speed before, has the law been slow to respond, and the AI companies so far have got away with it. Much like Uber, it seems they knew that if they moved fast and broke things, they could make their money and be established before the law caught up with them - whilst claiming to disrupt and innovate for the common good. It seems some in Silicon Valley have claimed the right to appropriate artists’ work in order to mechanically process it, and sell it back to us. 

> “All that we’ve been working on for so many years has been taken from us so easily with AI... It’s really hard to tell whether this will change the whole industry to the point where human artists will be obsolete. I think my work and future are under a huge question mark”

-- Greg Rutkowski

Following the outcry from the many artists whose work was used, OpenAI and Midjourney are now facing copyright infringement lawsuits from the likes of [Getty Images](https://www.theverge.com/2023/12/4/23988403/getty-lawsuit-stability-ai-copyright-infringement) and the [New York Times](https://www.nytimes.com/2023/12/27/business/media/new-york-times-open-ai-microsoft-lawsuit.html). AI companies thus far seem unrepentant, which is perhaps unsurprising given the profits they’re making.

#### **Democratising art**

The big three image generators claim some noble and lofty goals whilst making their money off of others’ work. Midjourney for example, claims to be _“expanding the imaginative powers of the human species”_ whilst StabilityAI say they are _“building the foundation to activate humanity’s potential.”_ The story sold to us is similar across the board - that art is being ‘democratised’ - pulled from the clutches of those entitled artists and mechanised - converted into a tool that will instead make everyone an artist.

By its fans, ‘AI art’ is often described as a new medium or frontier, where humans forgo the effort of making their own art or building any skill - instead pushing buttons to have art appear instantly. Rather than discovering the joy of creating, we’re told instead it’s better we learn to think as the computer thinks, and become experts at writing prompts because, hey.. it’s the same thing.

![](https://images.squarespace-cdn.com/content/v1/63b16acfbd1865531d7c3949/20b97652-e1a3-46f6-8aa8-03f8897a5d38/GettyImages-154384601-1024x843.jpg)

‘Les Demoiselles d’Avignon’’ by Pablo Picasso. A painting which disrupted modern art without hurting artists. Source: https://t.ly/zIiLI

By its nature, art has always been something society struggles to firmly define, and that makes any discussion around its definition difficult. As soon as we establish a common idea of what constitutes art, an artist like [Picasso](https://www.pablopicasso.org) or [Duchamp](https://www.metmuseum.org/toah/hd/duch/hd_duch.htm) is compelled to push the boundaries - making it an ever evolving and expanding field. 

But ‘AI art’ is something very different. Boundary-pushing art never threatened the careers of other artists before, or threatened to monopolise the means of creation - it simply challenged our ideas about ourselves and our world. New art might shock us or make us uncomfortable, but it’s never had this much destructive power, nor generated this much money for a handful of men so quickly.

> “Everyone is looking for the hack - the secret to success without hard work”

-- OpenAI CEO, Sam Altman

Art and illustration is everywhere in our world - it captures our imagination on book covers, brightens our homes, brings magazine articles to life, and adds richness to the environments of video games. The artists who create all of this are the under-appreciated workforce that breathe life and meaning into our everyday. They love their work, spend their entire lives honing their skills, and are often short-changed considering the value they give their employers. They’re expected to produce exceptional art to ever-shrinking timescales for often mediocre money. But they do it anyway, because this is what they love - bringing beauty and soul to the world.

![](https://images.squarespace-cdn.com/content/v1/63b16acfbd1865531d7c3949/b6a12284-e157-4128-b875-05fd4a537c43/6244760363584f5e7956ad4e_thisispaper-selected-ai-works-mario-klingemann-nft-05.jpg)

A piece by [Mario Klingemann](https://www.thisispaper.com/mag/selected-ai-works-mario-klingemann). Producing images with AI image generators, Mario describes himself as a full-time “AI artist.” Source: https://t.ly/53-mv

Over the last two years, commercial adoption of AI image generators has meant that real illustrators and artists are losing paid work and control of their own images. Now, the companies who would have previously hired artists can turn to a cheap, mechanical equivalent, and as long as this option is available some won’t be able to resist. AI images may be inferior in the details, but they’re incredibly cost efficient and near instantaneous. Automating this is about seeing art as simply ‘content,’ and what matters is that it keeps coming quickly and cheaply. Someone can be hired to touch-up any problems with the images at far less expense to the company. These workers are also lower skilled, disempowered and more interchangeable than they were before. 

This type of automation is a common corporate path. People don’t necessarily lose their jobs, but algorithmic management instead takes away their power, and lowers their pay and protections. 

Far from ‘democratising’ art, AI tools are instead having the opposite effect - privatising and automating it. AI is pushing skilled people out of the process and handing control to a handful of tech companies in a race to reduce and commodotise.

![](https://images.squarespace-cdn.com/content/v1/63b16acfbd1865531d7c3949/17ea1c10-f439-4bbd-9ddb-933b226dc859/1684447801448.png)

‘House of Earth and Blood’ by Sarah J. Maas. Publisher Bloomsbury used an AI-generated stock image for the book’s cover. Source: https://t.ly/smdNo

![](https://images.squarespace-cdn.com/content/v1/63b16acfbd1865531d7c3949/95af2628-1b25-4281-aeba-1d9e03a51305/MV5BNjE5YmJlYzItZjhlZC00NDk1LThiMmMtMDJjN2VmMmQxMTVjXkEyXkFqcGdeQXVyMTE0MzQwMjgz._V1_.jpg)

The entirely AI-generated title sequence to Marvel’s ‘Secret Invasion’ TV show. Source: https://t.ly/qFjR8

![](https://images.squarespace-cdn.com/content/v1/63b16acfbd1865531d7c3949/036e42b0-02e6-4c6a-8c8e-168b983149c6/x6v0nb52h4n91.png)

Videogame art assets, automatically generated with AI, instead of created by a game environmental artist. Source: https://shorturl.at/kARXZ

#### **Art is intrinsically human**

To call AI-generated images equivalent to real art, is - in my opinion - to entirely miss the point. The effortless, instantaneous nature of AI generation prevents it from having real meaning. It’s disposable. AI companies truly misunderstand art in thinking that the image is what matters, rather than the intent and the labour.

Art has always been intrinsically human. It comes as much from our flaws and mistakes as it does our successes. Through the process of making it, we express what’s inside us - our joy and frustration, longing and sadness - in a way which is instinctive and deep-rooted. It’s only through experiencing life as human beings that we have something to express and put to canvas or page. More often than not, we discover what the artwork is to be through the process of creation, rather than having a firm vision at the outset and simply assembling it, as a machine does.

We all know it when we see it - that painting that holds our attention or music that speaks to us. Through the sweat and the hours they put in, the artist communicates those feelings to us, the viewer, and that’s part of the joy of engaging with art. We feel understood and more connected as human beings as a result.

![](https://images.squarespace-cdn.com/content/v1/63b16acfbd1865531d7c3949/4a045a62-82c3-40e9-97b7-85e3089c8b30/ari-he-MamWbmmaylY-unsplash.jpg)

Photo by Ari He. Source: https://t.ly/3XFiS

Take away that substance, and what do you have? A lifeless matrix of pixels. With AI art, there is no feeling to communicate, no creative process, and therefore no value imbued in the ‘content.’

As any designer knows, the act of making art is also a way of observing, thinking and problem solving. The creative mindset is something which must be exercised, and which can be applied in so many other areas of life. It’s at the heart of all design and architecture. By letting a machine think for us, we are robbing ourselves of the joy and reward of creation.

AI models are an inherently conservative technology. By thinking on your behalf, and by reducing creative decisions to an algorithmic, wholly quantitative process, they severely constrain the possible outcomes, and no true artistic surprises or discoveries are possible. The models simply crunch the data they’re fed and serve a mash-up back to you. Nothing more.

#### **The inevitability myth**

But - AI enthusiasts have often told me - now the genie is out of the bottle, it' can’t be put back in. We should adopt AI or be left behind. The idea that we should fight to help destroy a vital part of our society’s culture so corporations can benefit, is deplorable to me. There’s nothing inevitable about technological change. Any development takes time, effort, money and determination, as well as a clear intent - continuously reinforced so that a team of people can work towards a common goal.

Technological change is a political and commercial process, shaped by the interests of the corporations and governments that surround us, who make deliberate and resolute choices. So the AI tools we have today are a choice, and a different choice can be made at any time. If we want to see AI work differently, or even pull the plug altogether, we can collectively do so.

![](https://images.squarespace-cdn.com/content/v1/63b16acfbd1865531d7c3949/92b18f56-39b6-4da7-b236-380be3f985e9/IMG_2115.JPG)

Source: https://shorturl.at/dizYZ

One has to wonder - when we could use AI models for so many different purposes, why try to automate illustration, and not something we’d all much rather not do, like tax returns? As artist Molly Crabapple said: _“I cannot understand why someone would burn a tonne of carbon, just to take away a job people love to do, and give it to a machine.”_ It’s entirely possible that Silicon Valley tech men just thought they were tinkering with an interesting maths challenge, and didn’t think what the consequences might be for other people. Perhaps these engineers share a reductionist world view, where the human brain is seen as a computer, and everything in life can be expressed through equations and code. Heartbreakingly, perhaps this even extends to art.

![](https://images.squarespace-cdn.com/content/v1/63b16acfbd1865531d7c3949/b21683be-d31a-4328-95a5-eac31514aabd/pexels-ivan-samkov-6816538.jpg)

Photo by Ivan Samkov. Source: https://shorturl.at/clEIN

#### **The long term impact**

If we continue on the path AI tools have set, then creative careers such as illustration and journalism will be far less viable, and far fewer young people will enter the creative fields. They may disappear entirely. 

Art is often undervalued today, but by automating it we drive that appreciation even further down. Art would be regarded as something that can be produced instantly at scale, and more worryingly, given precious little thought or effort. 

We’d have fewer artists, writers or musicians, and those we do have will be from the same wealthy, privileged segment of society. That narrowing of experience means any real art we get will be all the poorer, and with fewer people able to spend their time creating art, we can expect less evolution and growth of the field. There’d be no more Grayson Perrys or David Bowies. Art may ultimately become regarded as something machines do, not us - which is backwards and perverse.

> “As we invent more species of AI, we will be forced to surrender more of what is supposedly unique about humans. Each step of surrender—we are not the only mind that can play chess, fly a plane, make music, or invent a mathematical law—will be painful and sad.”

-- Kevin Kelly

As AI-generated images fill the internet, there will be less real art to train the models on, so if demand keeps growing, we can expect them to be fed more AI-generated images instead. It’s easy to see where that leads - a steady degradation of quality as copies are copied over and over again, and the richness of the original work fades from memory. AI art is ultimately a race to the bottom. 

Whilst the AI companies claim to be taking us towards the future, I for one find their vision to be cynical and cold. Follow this through, and it leads to a world where humans express creativity only through a corporate-owned platform which we pay for on a monthly subscription. Do we really want the means to create art to be taken and privatised? This is the direction we’re headed, unless we decide to say no.

#### **The fight back**

All of this is rather depressing - and it should be - but there is good news, too. Many people aren’t taking this lying down, and a strong and steadfast call for change is growing by the day.

At the grassroots level, professional artists and illustrators have been staging protests by filling community sites like [DevinatArt](https://www.deviantart.com) and [ArtStation](https://www.artstation.com?dimension=all&sort_by=community) with a tidal wave of protest placards. In addition, many of the companies that produce the software and sites creatives use, have found themselves needing to clarify their stance in the face of widespread unrest. Where companies have always claimed to be supportive of artists, now they’re being asked to prove it through their actions - by for example, banning AI art from their sites.

![](https://images.squarespace-cdn.com/content/v1/63b16acfbd1865531d7c3949/19a8ff9d-f0c3-4f5d-b4e3-96a52bb88007/Fj99C43XkAEBCQy.jpg)

Simultaneously, New-York based illustrator Molly Crabapple has posted [an open letter](https://artisticinquiry.org/AI-Open-Letter) and petition, calling for book and magazine publishers to take a stand with artists and refuse to use AI art. Letters like this have become focal points for thousands of supporters, and Crabapple’s includes signatures from high profile people such as author Naomi Klein and actor Jon Cusack. 

But some artists have been brave enough to take their challenge all the way to court. After discovering that her work has been used to train AI models without her consent, concept artist [Karla Ortiz](https://www.karlaortizart.com) \- along with two other artists - is driving a class-action lawsuit against StabilityAI, Midjourney and DeviantArt. Their legal challenge cites copyright infringement, unfair competition and reputational harm. Karla’s fight has helped bring public attention to the plight of artists and raise awareness about the real human impact of AI.

> “If we’re going to talk about what really stops people from pursuing art, it’s those economic issues. It’s issues like a lack of universal health care, few meaningful grants for artists, and the fact that most of us are just a missed paycheck away from homelessness and hunger. This doesn’t solve that.”

-- Karla Ortiz

![](https://images.squarespace-cdn.com/content/v1/63b16acfbd1865531d7c3949/49baa7c7-768f-4e80-8cb8-58088f220a51/karkla-ortiz-6.jpg)

Illustration by Karla Ortiz. Source: https://shorturl.at/avCX1

At the time of writing, big players such as [The New York Times](https://www.theverge.com/2023/12/27/24016212/new-york-times-openai-microsoft-lawsuit-copyright-infringement) and [Getty Images](https://www.farrer.co.uk/news-and-insights/getty-gets-tough-on-london-based-ai-firm/) have also launched their own lawsuits, adding significant weight behind the call for justice. With all this mounting pressure, it seems unlikely that image generators will be able to continue as they have been for much longer.

Last year, an art competition at the Colorado State Fair was unwittingly won by an Midjourney-generated image. Jason Allen, the man behind it, only admitted using AI after he had won the competition, and was subsequently disqualified. Nevertheless, he contested the decision amid heated debate over what constituted both ‘art’ and ‘artist,’ 

Around the same time, a man named Stephen Thaler tried to get US copyright for an image he produced with AI which he called ‘A Recent Entrance to Paradise.’ In court, he argued that an AI model should be granted a copyright because we’ve been using mechanical devices like cameras for years. He didn’t convince the federal judge however, who ruled that "human authorship is an essential part of a valid copyright claim."

I firmly agree with that judge. Whilst artists regularly use different devices to produce art, they are always the source of imagination and skill, and always directly controlling the tools. With AI art, the user is no longer in any real control of what comes out, and so should not be considered its author.

![](https://images.squarespace-cdn.com/content/v1/63b16acfbd1865531d7c3949/f50c76ec-7e7b-48d6-8e89-70ea5fa90725/merlin_212276709_3104aef5-3dc4-4288-bb44-9e5624db0b37-superJumbo.jpg)

‘Théâtre D’opéra Spatial’ - made by Midjourney from a prompt by Jason Allen - which contraversially won first prize in the 2023 Colorado State Fair art competition. Source: https://shorturl.at/fhkow

![](https://images.squarespace-cdn.com/content/v1/63b16acfbd1865531d7c3949/885b9bf8-8a24-404f-ae64-d60a98fabbe1/A_Recent_Entrance_to_Paradise.jpg)

‘A Recent Entrance to Paradise’ - made by AI from a prompt by Stephen Thaler - who tried and failed to win a copyright issue for the machine as an author. Source: https://shorturl.at/eoALO

Copyright law, design registration and similar legal frameworks exist to protect the livelihoods of creatives. They’re old and far from perfect, but nevertheless deserve to be protected. Copyright law reinforces the value of their work and ensures artists - many of whom are self-employed - get some small amount of pay when sick or something in retirement. If AI can just ignore copyright law, then it can take away what little income protection artists have.

Art software giant Adobe are now attempting a more ethical take on generative AI with [Firefly](https://www.adobe.com/products/firefly.html). Targeted at creatives, Firefly claims to retain an evidence trail linking results to the original input images, though how well this will work is still unclear. Adobe and others are also trying to establish some governance with a universal ‘Do Not Train’ tag which might protect artists’ work from being scraped by AI models. One has to ask though, why the onus should be on the artists to enforce protection, rather than reigning in AI in the first place.

![](https://images.squarespace-cdn.com/content/v1/63b16acfbd1865531d7c3949/3e5bd50a-8949-42d2-b142-90b39b737d70/PXi8WHvsh2tEM73ofdDk7C.jpg)

Firefly - Adobe’s first attempt at a more ethical image generator. Source: https://t.ly/mxNZq

Another way artists can protect their work is with [Glaze](https://glaze.cs.uchicago.edu/what-is-glaze.html). A software tool created by a team at the University of Chicago, Glaze allows artists to invisibly encrypt their digital artworks to prevent AI models using them.

Whilst the images remain unchanged to the human eye, they will now completely confuse AI models attempting to train on them. It’s an active digital watermark that just might afford creatives some genuine protection and peace of mind whilst the legal and commercial worlds struggle to catch up.

![](https://images.squarespace-cdn.com/content/v1/63b16acfbd1865531d7c3949/01efafd4-2f50-446c-89a7-be4692d3486f/glaze_ai_demo.jpg)

Glaze offers artists protection against unwanted scraping by AI models  
Source: https://shorturl.at/apvL3

#### **Constructive criticism**

I’m never one to just complain without offering a better suggestion, so the last thing to address is - what’s a more positive way forward?

Ideally, I would love to see AI image generators disappear in their current form. I’m a designer, and the reason I became one in the first place was to make technology revolve around people. If we don’t know how something will benefit people, then why should we bring it into the world in the first place? If it actively harms people, then we should stop immediately, and rethink what we’re doing. That’s why I’d like to see these tools rethought by people with a stronger understanding of the impact on working artists.

The money and resources put behind AI tools today could be redirected towards goals that really benefit society. It would be encouraging to see a human-centric exploration of how AI could be put to better use and to have the outcomes thought through, prototypes trialled and communities’ feedback sought, before anything is launched. 

I would like to see copyright law updated and reinforced to tackle this new type of problem, with a focus on protecting the livelihoods of working artists, and recognition of the value their art brings to society. Hopefully the lawsuits happening at the time of writing will set a precedent for this and lead to greater legal protections.

I learned recently that Stable Diffusion allows people to train their own custom AI models using their own training datasets. If people were legally required to own or have permission to use all the data they put into models, that would certainly be a step in the right direction - perhaps by requiring companies to keep copies of the original datasets - though this alone would not undo the damage that’s already been caused.

![](https://images.squarespace-cdn.com/content/v1/63b16acfbd1865531d7c3949/f5c16a4c-e85c-4973-a626-5c02ff9e964d/elena-mozhvilo-xw8DfYBaNbQ-unsplash.jpg)

Photo by Elena Mozhvilo. Source: https://shorturl.at/sxGN8

#### **A wonderful legacy**

But above all else, perhaps this drama could remind us all of the value of art and how it enriches our lives. We’re coming precariously close to destroying something that in no small part makes the world a place worth living in, and enables society to function through expression, reflection and connection. We’re lost without art, and we cannot afford to take it for granted.

What if we all decided to say no to AI, and instead worked up the courage to try and draw, or paint, or sculpt by ourselves? What if the strange, artificial taste of ‘AI art’ left us dissatisfied, and curious enough about creativity to start doodling with a biro in our notebook? It might feel good, and it might spark a flicker of creative confidence. After all, we all drew as children, but as we’ve got older, we’ve forgotten how.

What a wonderful legacy AI art might have, if it disappeared and made all of us into artists. Artists who need nothing more than pencil, paper and our own feelings as inspiration. We had everything we needed, all along.

* * *

### Further reading & listening

**Podcasts**

  * [Why AI is a Threat to Artists w/ Molly Crabapple](https://www.techwontsave.us/episode/174_why_ai_is_a_threat_to_artists_w_molly_crabapple) : Tech Won’t Save Us Podcast, Episode 174

  * [AI Criticism has a Decades-Long History w/ Ben Tarnoff](https://techwontsave.us/episode/182_ai_criticism_has_a_decades_long_history_w_ben_tarnoff) : Tech Won’t Save Us Podcast, Episode 182

  * [The Human Side of the AI Underclass w/ Joanne McNeil](https://techwontsave.us/episode/196_the_human_side_of_the_ai_underclass_w_joanne_mcneil) : Tech Won’t Save Us Podcast, Episode 196

  * [The Future of the State w/ James Plunkett](https://player.fm/series/jon-richardson-and-the-futurenauts-the-book-of-revelations-2655856/s3-ep4-the-future-of-the-state-with-james-plunkett) : Jon Richardson & The Futurenauts, Season 3, Episode 4




**Books**

  * [Blood in the Machine: The Origins of the Rebellion Against Big Tech](https://www.hachette.co.uk/titles/brian-merchant/blood-in-the-machine/9780316487740/) by Brian Merchant

  * [Resisting AI: An Anti-Fascist Approach to Artificial Intelligence](https://bristoluniversitypress.co.uk/resisting-ai) by Dan McQuillan

  * Hello World: Being Human in the Age of Algorithms – Hannah Fry




**Articles**

  * [AI-Generated Art Controversy: The Future of Creativity or a Replacement for Human Talent?](https://magazine.artland.com/ai-art-creativity-controversy/) by Adam Hencz for Artland

  * [Restrict AI from Publishing: An Open Letter](https://artisticinquiry.org/AI-Open-Letter) by Molly Crabapple for The Center for Artistic Inquiry and Reporting

  * [Independent Artists Are Fighting Back Against A.I. Image Generators With Innovative Online Protests](https://news.artnet.com/art-world/independent-artists-are-fighting-back-against-a-i-image-generators-with-innovative-online-protests-2231334) by Richard Whiddington for Artnet

  * [Molly Crabapple Has Posted an Open Letter by 1,000 Cultural Luminaries Urging Publishers to Restrict the Use of ‘Vampirical’ A.I.-Generated Images](https://news.artnet.com/art-world/open-letter-urges-publishers-not-to-use-ai-generated-illustrations-2294392) by Jo Lawson-Tancred for Artnet

  * [No, teaching AI to copy an artist’s style isn’t ‘democratization.’ It’s theft](No, teaching AI to copy an artist’s style isn’t ‘democratization.’ It’s theft) by Soleil Ho for The San Fransisco Chronicle

  * [Stability AI swerves copyright infringement allegations in response to Getty lawsuit ](https://sifted.eu/articles/stability-getty-lawsuit)by Tim Smith and Kai Nicol-Schwarz for Sifted

  * [Glaze protects art from prying AIs](https://techcrunch.com/2023/03/17/glaze-generative-ai-art-style-mimicry-protection/?guccounter=1&guce_referrer=aHR0cHM6Ly93d3cuZ29vZ2xlLmNvbS8&guce_referrer_sig=AQAAAL-Ny1v1GgTFP5RHGwUaBiuuLdi6sQPsn-WB7FAXZ1l8Dk7zdNS0JvTkqf7ur9_Jbe6zAJE-iTflY8zmysDfgxdcsebQORNb63qpfPVGxj7nccpCOzHb7gJl2Jp_CaXetIFMe8sj8Wr4Me_yq7zP4KTJVkRnmn7EQkaOnBWNuj7c) by Natasha Lomas for TechCrunch

  * [US judge: Art created solely by artificial intelligence cannot be copyrighted'](https://arstechnica.com/tech-policy/2023/08/us-judge-art-created-solely-by-artificial-intelligence-cannot-be-copyrighted/) by Jon Brodkin for ArsTechnica

  * [The US Copyright Office says an AI can’t copyright its art](https://www.theverge.com/2022/2/21/22944335/us-copyright-office-reject-ai-generated-art-recent-entrance-to-paradise) by Adi Robertson for The Verge

  * [Weizenbaum’s Nightmares: How the inventor of the first chatbot turned against AI ](https://www.theguardian.com/technology/2023/jul/25/joseph-weizenbaum-inventor-eliza-chatbot-turned-against-artificial-intelligence-ai)by Ben Tarnoff for The Guardian

  * [AI Machines aren’t ‘hallucinating,’ but their makers are](https://www.theguardian.com/commentisfree/2023/may/08/ai-machines-hallucinating-naomi-klein) by Naomi Klein forThe Guardian

  * [How Adobe is managing the AI copyright dilemma, with general counsel Dana Raoby](https://www.theverge.com/24027198/adobe-dana-rao-ai-copyright-fair-use-figma-acquisition-deal-decoder-interview) Nilay Patel for The Verge

  * [Why Generative AI Angers Artists but Not Writer](https://thealgorithmicbridge.substack.com/p/why-generative-ai-angers-artists)s by Alberto Romero for The Algorithmic Bridge

  * [AI: Digital artist's work copied more times than Picasso](https://www.bbc.co.uk/news/uk-wales-66099850) by Clare Hutchinson & Phil John for BBC News

  * [This artist is dominating AI-generated art. And he’s not happy about it](https://www.technologyreview.com/2022/09/16/1059598/this-artist-is-dominating-ai-generated-art-and-hes-not-happy-about-it/) by Melissa Heikkiläarchive page for MIT Technology Review

  * [Artists slam Marvel over AI-generated credits in Secret Invasion](https://www.thetimes.co.uk/article/marvel-secret-invasion-ai-generated-opening-credits-sequence-backlash-w3bn5fdb2?gad_source=5&gclid=EAIaIQobChMIhaa7udDXgwMVqTkGAB1VeguJEAAYASAAEgKVcvD_BwE) by Mark Sellman for The Times

  * [Courts are using AI to sentence criminals. That must stop now](https://www.wired.com/2017/04/courts-using-ai-sentence-criminals-must-stop-now/) by Jason Tashea for Wired

  * [The New York Times is suing OpenAI and Microsoft for copyright infringement](https://www.theverge.com/2023/12/27/24016212/new-york-times-openai-microsoft-lawsuit-copyright-infringement) by Emma Roth for The Verge

  * [Getty gets tough on London-based AI firm](https://www.farrer.co.uk/news-and-insights/getty-gets-tough-on-london-based-ai-firm/) by William Charrington and Hoi-Yee Roper for Farrer & Co




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url: http://arxiv.org/abs/2412.01450v1
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[2412.01450v1] Artificial Intelligence for Geometry-Based Feature Extraction, Analysis and Synthesis in Artistic Images: A Survey

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# Computer Science > Artificial Intelligence

**arXiv:2412.01450v1** (cs) 

[Submitted on 2 Dec 2024]

# Title:Artificial Intelligence for Geometry-Based Feature Extraction, Analysis and Synthesis in Artistic Images: A Survey

Authors:[Mridula Vijendran](https://arxiv.org/search/cs?searchtype=author&query=Vijendran,+M), [Jingjing Deng](https://arxiv.org/search/cs?searchtype=author&query=Deng,+J), [Shuang Chen](https://arxiv.org/search/cs?searchtype=author&query=Chen,+S), [Edmond S. L. Ho](https://arxiv.org/search/cs?searchtype=author&query=Ho,+E+S+L), [Hubert P. H. Shum](https://arxiv.org/search/cs?searchtype=author&query=Shum,+H+P+H)

View a PDF of the paper titled Artificial Intelligence for Geometry-Based Feature Extraction, Analysis and Synthesis in Artistic Images: A Survey, by Mridula Vijendran and 4 other authors

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> Abstract:Artificial Intelligence significantly enhances the visual art industry by analyzing, identifying and generating digitized artistic images. This review highlights the substantial benefits of integrating geometric data into AI models, addressing challenges such as high inter-class variations, domain gaps, and the separation of style from content by incorporating geometric information. Models not only improve AI-generated graphics synthesis quality, but also effectively distinguish between style and content, utilizing inherent model biases and shared data traits. We explore methods like geometric data extraction from artistic images, the impact on human perception, and its use in discriminative tasks. The review also discusses the potential for improving data quality through innovative annotation techniques and the use of geometric data to enhance model adaptability and output refinement. Overall, incorporating geometric guidance boosts model performance in classification and synthesis tasks, providing crucial insights for future AI applications in the visual arts domain. 

Comments: | 56 pages, 8 tables, 1 figure (35 embedded images), Artificial Intelligence Review (AIR) 2024  
---|---  
Subjects: |  Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)  
Cite as: | [arXiv:2412.01450](https://arxiv.org/abs/2412.01450) [cs.AI]  
  | (or  [arXiv:2412.01450v1](https://arxiv.org/abs/2412.01450v1) [cs.AI] for this version)   
  |  <https://doi.org/10.48550/arXiv.2412.01450> Focus to learn more arXiv-issued DOI via DataCite  
  
## Submission history

From: Mridula Vijendran [[view email](/show-email/3db9f329/2412.01450)]   
**[v1]** Mon, 2 Dec 2024 12:41:15 UTC (13,545 KB)  


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title: How Generative AI Will Change The Jobs Of Artists And Designers
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How Generative AI Will Change The Jobs Of Artists And Designers

By[Bernard Marr](https://www.forbes.com/sites/bernardmarr/)

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# How Generative AI Will Change The Jobs Of Artists And Designers

By[Bernard Marr](https://www.forbes.com/sites/bernardmarr/)

, Contributor. 

Forbes contributors publish independent expert analyses and insights.

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May 28, 2024, 02:06am EDT

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How Generative AI Will Change The Jobs Of Artists And Designers

Adobe Stock

By definition, artists and designers are creative people. They work in these jobs because they have talent and skills that they love to share with the world.

So, it isn’t surprising that many of them are cautious or distrustful about the impact generative AI will have on their professional lives.

However, like writers, architects, and other creative professionals, many find that it creates opportunities to work more efficiently and even enhance their creativity.

Generative AI is transforming most industries, and the creative fields of art and design are certainly no exception. But by embracing it, artists, designers, illustrators and animators could find themselves spending more of their time on the aspects of their work they are passionate about and less on the routine and mundane elements they'd rather skip over.

  


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## **Increased Productivity And Efficiency**

According to [research](https://www.weforum.org/agenda/2023/05/generative-ai-creative-jobs/ "https://www.weforum.org/agenda/2023/05/generative-ai-creative-jobs/") by Goldman Sachs, generative AI has the potential to automate 26 percent of the tasks carried out by professional artists and designers.

This is likely to include things like recoloring, editing images to remove backgrounds and unwanted features, enhancing image quality, adapting visuals to fit different style templates, creating multiple variations of existing work for use in different jobs, and generating design elements such as icons, fonts and textures.

It will still be up to the artist or designer to put visual assets together in a way that’s personal to them, but by augmenting their skills in this way, artists will be able to work more quickly and make better use of their time.

Generative AI functionality is also quickly being incorporated into creativity tools and software platforms, like [Photoshop](https://news.adobe.com/news/news-details/2024/New-Adobe-Photoshop-with-Advanced-Generative-Fill-and-Generate-Image-Brings-New-Superpowers-to-All/default.aspx "https://news.adobe.com/news/news-details/2024/New-Adobe-Photoshop-with-Advanced-Generative-Fill-and-Generate-Image-Brings-New-Superpowers-to-All/default.aspx") and [AutoCAD](https://www.forbes.com/sites/robertdefrancesco/2024/03/28/autodesk-infuses-generative-ai-across-its-expansive-product-portfolio/ "https://www.forbes.com/sites/robertdefrancesco/2024/03/28/autodesk-infuses-generative-ai-across-its-expansive-product-portfolio/"). This makes these tools more user-friendly and intuitive, meaning artists and designers spend less time working out how to get the software to do what they want and more time creating.

For businesses, this will mean quicker project completion, while freelancers will fit more work into their schedules, allowing them to take on more work or offer lower prices to their clients.

  


## **Enhancing Creativity**

Generative AI can be great for sparking the imagination. Artists and designers staring at a blank page and wondering how to get started may find it useful for suggesting initial concepts, sketches, characters or color palettes. Unusual or unexpected ideas generated in this way could encourage them to take their creativity in new directions and explore uncharted territory!

It can help to overcome “creative block” and think about new ways to approach creative briefs and challenges.

It can give artists and designers an idea of how their work will change if they employ different styles or techniques, allowing them to experiment with new aesthetics and expand their skill sets.

It can even act as a personalized art critic, offering feedback and suggestions on how work can be improved or altered in order to better align with project goals.

While generative AI can’t replace human creativity – and neither would we want it to – it can be used in ways that make us think about what it is that specifically makes work “human”, enabling us to focus on expressing those elements of our work.

  


## **New Forms Of Collaborative Art And Design**

Generative AI opens the doors to entire new artforms and genres of art. [Memories of Passers By I](https://medium.com/dipchain/mario-klingemann-memories-of-passersby-i-c73f72675743 "https://medium.com/dipchain/mario-klingemann-memories-of-passersby-i-c73f72675743"), by Mario Klingemann, became one of the first pieces of entirely AI-generated art to be exhibited publicly when it was unveiled in 2019. It uses a generative adversarial network (GAN) to create a never-ending stream of unique portraits.

More recently, the New York Museum of Modern Art acquired a piece titled Unsupervised Machine Hallucinations by [Refik Anadol](https://www.economist.com/culture/2024/02/20/refik-anadols-use-of-ai-has-made-him-the-artist-of-the-moment "https://www.economist.com/culture/2024/02/20/refik-anadols-use-of-ai-has-made-him-the-artist-of-the-moment"), which uses generative AI to reinterpret existing works in the museum’s collection.

And the artist Sougwen Chun received acclaim for her project [Drawing Operations](https://sougwen.com/project/drawingoperations2018 "https://sougwen.com/project/drawingoperations2018"), for which she built an AI-powered robotic arm that collaborates with her during drawing sessions, learning from her movements and contributing its own strokes in order to create collaborative works of art.

As technology progresses, we can expect to see museums and galleries develop increasingly immersive and interactive experiences, blurring the boundaries between the real and the virtual, as well as artists and audiences. Imagine installations that adapt, depending on who is viewing them, to create personalized experiences, incorporating technologies like augmented and virtual reality alongside generative AI.

Just as the invention of photography gave rise to impressionism, followed by movements such as cubism, surrealism, and pop art, artists working in traditional mediums will have their own response to the arrival of the AI age, creating entirely new artistic frontiers.

It probably isn’t going too far to say that the convergence of human and machine creativity may mark the start of a new chapter in the field of art and design. Rather than stifling human imagination and ingenuity, I believe that it will stoke them, leading to new art forms, genres and movements and forcing us to reassess the age-old question of what exactly constitutes art!

  


## **How Will The Roles Of Artists And Designers Change?**

Well, let’s start with how they will stay the same. Artists and designers will continue to be responsible for bringing creative visions, concepts and ideas into the world, infusing them with meaning, emotion and relevance.

Nevertheless, they are likely to see their roles change, with the focus shifting toward providing creative direction, conceptualization and critical thinking.

As with other professions, they are likely to need to learn new skillsets in order to work alongside machines effectively and safely. This will include prompt engineering, in order to get the most out of the new tools that are available.

They are also likely to need an understanding of the principles of AI ethics. Particularly, where the line is drawn when it comes to authorship and preserving the essential human aspects of art. But also around IP issues, AI bias and transparency.

Ultimately, while they will embrace new technical skills and adapt to new workflows, the essence of their roles – to channel human perspectives, experiences and feelings into captivating works of art – will remain unchanged.

This is why I believe that the dawn of the AI era doesn’t mark the end of human creativity but the start of a hugely exciting new chapter.

Follow me on [Twitter](https://www.twitter.com/BernardMarr) or [LinkedIn](https://www.linkedin.com/in/bernardmarr/). Check out my [website](https://bernardmarr.com/) or [some of my other work](https://www.amazon.com/stores/Bernard-Marr/author/B001H6KUSS?ref=ap_rdr&store_ref=ap_rdr&isDramIntegrated=true&shoppingPortalEnabled=true).

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# Pros and cons of AI-generated content

## Artificial intelligence is growing rapidly in the tech sector and assisting with various tasks such as creating images and generating content. However, it cannot do everything.

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Published: 08 Jul 2024

[AI-generated content](https://www.techtarget.com/searchenterpriseai/feature/Real-world-GenAI-applications-across-leading-industries) is becoming popular, but many people wonder how to use it and if it truly sounds authentic.

[AI](https://www.techtarget.com/searchenterpriseai/definition/AI-Artificial-Intelligence) is growing rapidly in the tech sector, including supply chain, food industry, chatbot, and content and image generation use cases. AI-generated images made the news when critics claimed AI art systems were stealing people's intellectual property on large models, such OpenAI's [Dall-E](https://www.techtarget.com/searchenterpriseai/definition/Dall-E).

OpenAI introduced a similar AI program for content known as [ChatGPT](https://www.techtarget.com/whatis/definition/ChatGPT). This bot serves a more general purpose and offers an open prompt for users to ask questions, give a command or ask for an observation. The bot answers both broad and specific questions. The system generated answers that professors and other educators said were extensive enough to receive a good grade. However, after further review, some sources -- such as Stack Overflow for coders and developers -- said the information was incorrect and temporarily banned answers using this system. Since there are no limits to the answers ChatGPT can give, a user was even allowed to search how to make explosives and shoplift.

[Businesses are looking at AI tools](https://www.techtarget.com/searchnetworking/feature/What-AI-capabilities-do-network-tools-need) to help with content creation and copywriting. The same question is being posed as to [whether AI will take the place](https://www.techtarget.com/searchenterpriseai/opinion/Will-AI-replace-technical-writers-One-users-experience) of content writers. While AI-generated content might not be suitable for all copywriting, there are some great ways to use these tools.

## __What is AI-generated content?

AI-generated content includes copy such as blogs, marketing materials, articles and product descriptions written by a machine. The AI content creator generates the content after a person inputs information such as keywords, phrases and topics.

This article is part of

### __[What is Gen AI? Generative AI explained](https://www.techtarget.com/searchenterpriseai/definition/generative-AI)

  * Which also includes:
  * [8 top generative AI tool categories for 2025](https://www.techtarget.com/searchenterpriseai/tip/Top-generative-AI-tool-categories)
  * [Will AI replace jobs? 17 job types that might be affected](https://www.techtarget.com/whatis/feature/Will-AI-replace-jobs-9-job-types-that-might-be-affected)
  * [25 of the best large language models in 2025](https://www.techtarget.com/whatis/feature/12-of-the-best-large-language-models)



AI-generated content can help speed up the writing process, and businesses are starting to take notice. Marketers might consider [using AI-generated content for automating the content marketing process](https://www.techtarget.com/searchcustomerexperience/feature/How-to-use-generative-AI-for-marketing) \-- which can, at times, be time-consuming and expensive.

## __How does AI-generated content work?

To produce content, users enter the text format needed, along with a topic and keywords. The AI tool then searches the internet and pieces together copy fitting the requested format. Some tools also rewrite a company's existing content to add information based on web searches and editing tools. These tools can take this existing content and rework it to fit other communication formats, such as turning a blog article into a social media post or email draft.

There are free content generators available, but others charge for more in-depth content.

## __What are the pros of AI-generated content?

AI-generated content can be valuable in several ways. Here are some of the pros.

### Efficiency and scalability

AI can create content much faster than people, which is probably the biggest benefit. An AI tool can produce an article in minutes. It would take a human writer much longer to do all the research and write it.

Multiply the quick turnaround by the number of articles, and an AI tool can produce a significant amount of content.

AI also helps with language localization for various geographic areas and can create social media, personalizing this content for various sites.

### Cost-effectiveness

Hiring quality content writers typically costs a few hundred dollars per project, often depending on article length, the number of pieces and the needed technical knowledge. And this could prove to be money well spent if it results in high-quality, well-researched content.

Some AI writing tools are free, while others charge a monthly subscription rate. The pricing typically runs about $100 for tens of thousands of words.

Given these considerations, AI-generated content might be better suited for simpler content than articles needing expertise and authority.

### Improved SEO

AI content generators scroll through thousands of online documents to absorb information. In viewing all these documents, generators choose keywords to improve search engine optimization ([SEO](https://www.techtarget.com/whatis/definition/search-engine-optimization-SEO)). The AI tool can suggest keywords for the content writer. By using these keyword suggestions throughout an article, the content appears higher in search engine rankings if it follows the rest of the guidelines of being authoritative and is written by a person.

### Help with writer's block

Writer's block is a common hurdle for many people. At times, writers might have trouble creating authoritative content for a subject they know little about.

To help overcome this hurdle, AI tools can create detailed outlines and key points to help the writer determine what should be included in the article. These suggestions from AI tools can help the user overcome writer's block and spark the ideas needed to get started.

## __What are the cons of AI-generated content?

There are several considerations when it comes to using AI-generated content. Some content is best written by a human writer. Here are some [cons of AI-generated content](https://www.techtarget.com/searchenterpriseai/tip/Top-advantages-and-disadvantages-of-AI).

### Quality concerns and possible plagiarism

[AI relies on data and algorithms](https://www.techtarget.com/searchenterpriseai/tip/Types-of-AI-algorithms-and-how-they-work) for content. AI tools can cover black-and white-areas of a topic, but gray areas are more subjective and the intended tone can get lost.

Search engines might also flag content because it is similar to published materials, as the AI pulls from the same sources. The AI tools piece content together from various sites and reword them. Without adding the proper flow, this process goes against Google's "stitching and combining content" guidelines. Content needs to be authoritative and informative, which can be hard to do when piecing information together from various sites without proper human review.

### Algorithms devalue content

Google released its helpful content update in August 2022 to punish content created for the purpose of ranking higher in search engine results.

In March 2024, Google once again rolled out an update to improve some of its content ranking systems. The update is intended to help users decide if webpages aren't useful, provide a poor user experience or seem like they prioritize keywords over being informative to the reader.

### Lack of creativity and personalization

Creative content makes articles more engaging. People tend to share articles they feel a connection to, but AI does not have the [emotional intelligence](https://www.techtarget.com/searchcio/definition/emotional-intelligence) to create a story. Its focus is generally directed toward adding facts to an outline.

AI relies on existing web content and data to develop wording. The technology does not understand user intent for queries and still lacks the common sense of human behavior.

### Human editing still required

People still [need to read through AI-generated content](https://www.techtarget.com/whatis/feature/Steps-in-fact-checking-AI-generated-content). Although AI might save time, humans must continue to review articles for quality.

AI tools combine information from several websites into one piece. There might be some mix-ups to fix, such as product descriptions with textures and colors. This is generally because AI tools do not understand adjective meanings.

### Can't generate new ideas

AI tools use existing data for content, so this means they cannot come up with fresh ideas. AI tools make it hard to come up with new content covering the latest trending ideas and topics.

## __Ways to use AI-generated content

AI-generated content is best used as a writing assistant instead of a way to rely strictly on the technology. Here are some ways to use AI tools for assistance with content:

  * **Research.** For writers having issues organizing a topic or coming up with ideas, AI-generated content can help them get started. Some tools give ideas about what to include for broader topics to help narrow down the research process.
  * **Proofread current material.** To make sure a drafted article is optimized, writers can run it through AI tools for a grade. The tool can also highlight keywords and phrases that should be used. AI tools can further assist with checking grammar and correcting spelling mistakes.
  * **Write short content.** AI tools can produce a lot of content in a short amount of time, so they are a great way to reduce boredom with repetitive tasks. While some communications require more of an emotional side, some short descriptions do not. Product descriptions, metatags, ad copy and social media posts are examples of short text suitable for content generators.
  * **Translate language.** For written material to appeal to all audiences, AI generators can help translate content into different languages.
  * **Create templates.** AI tools can help create emails or other templates. Some AI tools offer different types of ready-made templates for users to plug in customized information.



## __Example of AI-generated content

There are many [AI content generators available](https://www.techtarget.com/whatis/feature/AI-content-generators-to-explore). Here is an example of how content generation works using a program called [CopyAI](https://www.copy.ai/):

  1. Select style of writing from the left side.
  2. Answer topic.
  3. Add main points.
  4. Select tone.

__A screenshot of the CopyAI content generator with information from creating an article.

__

A couple options pop up for text. Here is the AI-generated content from the information entered. This was the first option meant to imitate the information in this article:

> _AI is an amazing technology that has the potential to change our world and how we live in it. But like any new technology, there are pros and cons to AI-generated content -- and businesses need to be aware of both before deciding whether or not to use it._
> 
> _First, the pros:_
> 
> _1) AI-generated content saves time and money for businesses by allowing them to produce more content with fewer resources._
> 
> _2) It allows businesses to create highly personalized content for each customer, which leads to happier customers._
> 
> _3) AI-generated content makes sure that all information is correct, which helps prevent expensive lawsuits or bad press if something goes wrong with your business's operations (e.g., if you send out an email with incorrect information)._
> 
> _Now, let's talk about some of the cons:_
> 
> _1) AI-generated content can sound unnatural (like it was written by a robot), which can make customers feel uncomfortable or distrustful of your business. It doesn't matter how much effort you put into making sure that everything is correct if the end result sounds robotic!_
> 
> _2) If someone finds out that they've been reading an AI-generated piece of content instead._

Much of the information included in this TechTarget article is outlined in the AI-generated content above. However, the AI content is not particularly comprehensive or conversational -- even when selecting a friendly tone. This TechTarget article goes into greater detail for explanations and provides examples. The AI-generated content seems to be more of an outline and needs additional information to flow like a human-written article.

## __How to detect AI-generated content

There is a larger concern with AI-generated content: students using these tools to cheat. Several professors and teachers stated that the AI-generated content on ChatGPT created convincing essays.

To help distinguish between human and AI text, OpenAI [launched](https://openai.com/blog/new-ai-classifier-for-indicating-ai-written-text/) a new AI text classifier on Jan. 31, 2023. However, in July 2023, the company pulled the text classifier due to low accuracy. There are other online tools to help [detect AI-generated content](https://www.techtarget.com/searchenterpriseai/feature/How-to-detect-AI-generated-content) by classifying how likely it was written by a person versus AI. These resources include Originality.ai, Writing.com and Copyleaks.

OpenAI is also working on creating a watermark for longer AI-generated text as an immediate identifier.

In addition to running text through an online AI-content detector, there are a few other signs that content is AI-generated, such as the following:

  * **Lack of typos.** Human writing often has typos and contains slang terms. AI-generated content rarely has typos such as grammar mistakes or misspellings.
  * **Overuse of "the."** AI-generated content is based on predictive language by determining which word should be next, so it commonly uses words such as "the," "it" or "is" instead of more unusual terms.
  * **No cited sources.** AI-generated text will often give facts and not cite the sources.
  * **Shorter sentences.** AI content sentence length is typically shorter because it is trying to mimic human writing but often appears choppier.
  * **Repetition of words or phrases.** If certain words or phrases are used abundantly, this could be a sign of AI-generated content because it is trying to fill space with relevant keywords.
  * **Lack of analysis.** AI can collect data but cannot analyze it. If an article feels full of facts without any insight, it might be AI-generated.



**Editor's note:** _This article was updated in July 2024 to improve the reader experience._

_Amanda Hetler is a senior editor and writer for WhatIs where she writes technology explainer articles and works with freelancers._

#### __Next Steps

[Generative AI landscape: Potential future trends](https://www.techtarget.com/searchenterpriseai/tip/Generative-AI-landscape-Potential-future-trends)

[Top generative AI benefits for business](https://www.techtarget.com/searchenterpriseai/tip/7-top-generative-AI-benefits-for-business)

[Assessing different types of generative AI applications](https://www.techtarget.com/searchenterpriseai/tip/Assessing-different-types-of-generative-AI-applications)

[Generative AI challenges that businesses should consider](https://www.techtarget.com/searchenterpriseai/tip/Generative-AI-challenges-that-businesses-should-consider)

[Successful generative AI examples worth noting](https://www.techtarget.com/searchenterpriseai/tip/Successful-generative-AI-examples-worth-noting)

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url: http://arxiv.org/abs/2308.05201v2
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[2308.05201v2] "Generate" the Future of Work through AI: Empirical Evidence from Online Labor Markets

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# Computer Science > Artificial Intelligence

**arXiv:2308.05201v2** (cs) 

[Submitted on 9 Aug 2023 ([v1](https://arxiv.org/abs/2308.05201v1)), last revised 6 Jun 2024 (this version, v2)]

# Title:"Generate" the Future of Work through AI: Empirical Evidence from Online Labor Markets

Authors:[Jin Liu](https://arxiv.org/search/cs?searchtype=author&query=Liu,+J) (1), [Xingchen Xu](https://arxiv.org/search/cs?searchtype=author&query=Xu,+X) (2), [Xi Nan](https://arxiv.org/search/cs?searchtype=author&query=Nan,+X) (2), [Yongjun Li](https://arxiv.org/search/cs?searchtype=author&query=Li,+Y) (1), [Yong Tan](https://arxiv.org/search/cs?searchtype=author&query=Tan,+Y) (2) ((1) University of Science and Technology of China, (2) University of Washington)

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> Abstract:Large Language Model (LLM) based generative AI, such as ChatGPT, is considered the first generation of Artificial General Intelligence (AGI), exhibiting zero-shot learning abilities for a wide variety of downstream tasks. Due to its general-purpose and emergent nature, its impact on labor dynamics becomes complex and difficult to anticipate. Leveraging an extensive dataset from a prominent online labor market, we uncover a post-ChatGPT decline in labor demand, supply, and transactions for submarkets pertaining to text-related and programming-related jobs, in comparison to those not directly exposed to ChatGPT's core functionalities. Meanwhile, these affected submarkets exhibit a discernible increase in the complexity of the remaining jobs and a heightened level of competition among freelancers. Intriguingly, our findings indicate that the diminution in the labor supply pertaining to programming is comparatively less pronounced, a phenomenon ascribed to the transition of freelancers previously engaged in text-related tasks now bidding for programming-related opportunities. Although the per-period job diversity freelancers apply for tends to be more limited, those who successfully navigate skill transitions from text to programming demonstrate greater resilience to ChatGPT's overall market contraction impact. As AI becomes increasingly versatile and potent, our paper offers crucial insights into AI's influence on labor markets and individuals' reactions, underscoring the necessity for proactive interventions to address the challenges and opportunities presented by this transformative technology. 

Comments: | 65 pages, 6 figures, 22 tables  
---|---  
Subjects: |  Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); General Economics (econ.GN)  
ACM classes: | J.4  
Cite as: | [arXiv:2308.05201](https://arxiv.org/abs/2308.05201) [cs.AI]  
  | (or  [arXiv:2308.05201v2](https://arxiv.org/abs/2308.05201v2) [cs.AI] for this version)   
  |  <https://doi.org/10.48550/arXiv.2308.05201> Focus to learn more arXiv-issued DOI via DataCite  
  
## Submission history

From: Xingchen Xu [[view email](/show-email/e20c968c/2308.05201)]   
**[[v1]](/abs/2308.05201v1)** Wed, 9 Aug 2023 19:45:00 UTC (212 KB)  
**[v2]** Thu, 6 Jun 2024 22:23:57 UTC (2,024 KB)  


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url: http://arxiv.org/abs/2307.06066v1
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hash: fe75038fbf45aa5133b9e5140faa39b7fc119600aeec585083d46bab03b93652
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[2307.06066v1] Security in Online Freelance Software Development: A case for Distributed Security Responsibility

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# Computer Science > Cryptography and Security

**arXiv:2307.06066v1** (cs) 

[Submitted on 12 Jul 2023]

# Title:Security in Online Freelance Software Development: A case for Distributed Security Responsibility

Authors:[Irum Rauf](https://arxiv.org/search/cs?searchtype=author&query=Rauf,+I), [Tamara Lopez](https://arxiv.org/search/cs?searchtype=author&query=Lopez,+T), [Thein Tun](https://arxiv.org/search/cs?searchtype=author&query=Tun,+T), [Marian Petre](https://arxiv.org/search/cs?searchtype=author&query=Petre,+M), [Bashar Nuseibeh](https://arxiv.org/search/cs?searchtype=author&query=Nuseibeh,+B)

View a PDF of the paper titled Security in Online Freelance Software Development: A case for Distributed Security Responsibility, by Irum Rauf and Tamara Lopez and Thein Tun and Marian Petre and Bashar Nuseibeh

[View PDF](/pdf/2307.06066v1)

> Abstract:Secure software is a cornerstone to safe and resilient digital ecosystems. It offers strong foundation to protect users' sensitive data and guard against cyber-threats. The rapidly increasing landscape of digital economy has encouraged developers from different socio-technical and socio-economic backgrounds to join online freelance marketplaces. While, secure software practices facilitate software developers in developing secure software, there is paucity of research on how freelance developers adhere to security practices and how they can be facilitated to improve their security behavior in under-resourced environments. Moreover, freelance developers are often held responsible for producing insecure code. In this position paper, we review existing literature and argue for the case of distributed security responsibilities in online freelance environment. We propose a research agenda aimed at offering an organized and systematic effort by researchers to address security needs and challenges of online freelance marketplaces. These include: characterising software security and defining separation of responsibilities, building trust in online freelance development communities, leveraging the potential of online freelancing platforms in the promotion of secure software development and building adaptive security interventions for online freelance software development. The research has the potential to bring forth existing security solutions to wider developer community and deliver substantial benefits to the broader security ecosystem. 

Subjects: |  Cryptography and Security (cs.CR); Computers and Society (cs.CY)  
---|---  
Cite as: | [arXiv:2307.06066](https://arxiv.org/abs/2307.06066) [cs.CR]  
  | (or  [arXiv:2307.06066v1](https://arxiv.org/abs/2307.06066v1) [cs.CR] for this version)   
  |  <https://doi.org/10.48550/arXiv.2307.06066> Focus to learn more arXiv-issued DOI via DataCite  
  
## Submission history

From: Irum Rauf Dr. [[view email](/show-email/318e94b3/2307.06066)]   
**[v1]** Wed, 12 Jul 2023 10:35:27 UTC (389 KB)  


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url: http://arxiv.org/abs/2401.15497v5
scrape_date: 2025-03-11T13:08:34.367562
content_type: markdown
hash: 94adc90751df1885f85ac9b4339e896902944fe8b0dee455a5af135fc5d7c8a2
title:
[2401.15497v5] Foregrounding Artist Opinions: A Survey Study on Transparency, Ownership, and Fairness in AI Generative Art

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# Computer Science > Computers and Society

**arXiv:2401.15497v5** (cs) 

[Submitted on 27 Jan 2024 ([v1](https://arxiv.org/abs/2401.15497v1)), last revised 1 Oct 2024 (this version, v5)]

# Title:Foregrounding Artist Opinions: A Survey Study on Transparency, Ownership, and Fairness in AI Generative Art

Authors:[Juniper Lovato](https://arxiv.org/search/cs?searchtype=author&query=Lovato,+J), [Julia Zimmerman](https://arxiv.org/search/cs?searchtype=author&query=Zimmerman,+J), [Isabelle Smith](https://arxiv.org/search/cs?searchtype=author&query=Smith,+I), [Peter Dodds](https://arxiv.org/search/cs?searchtype=author&query=Dodds,+P), [Jennifer Karson](https://arxiv.org/search/cs?searchtype=author&query=Karson,+J)

View a PDF of the paper titled Foregrounding Artist Opinions: A Survey Study on Transparency, Ownership, and Fairness in AI Generative Art, by Juniper Lovato and 4 other authors

[View PDF](/pdf/2401.15497v5) [HTML (experimental)](https://arxiv.org/html/2401.15497v5)

> Abstract:Generative AI tools are used to create art-like outputs and sometimes aid in the creative process. These tools have potential benefits for artists, but they also have the potential to harm the art workforce and infringe upon artistic and intellectual property rights. Without explicit consent from artists, Generative AI creators scrape artists' digital work to train Generative AI models and produce art-like outputs at scale. These outputs are now being used to compete with human artists in the marketplace as well as being used by some artists in their generative processes to create art. We surveyed 459 artists to investigate the tension between artists' opinions on Generative AI art's potential utility and harm. This study surveys artists' opinions on the utility and threat of Generative AI art models, fair practices in the disclosure of artistic works in AI art training models, ownership and rights of AI art derivatives, and fair compensation. Results show that a majority of artists believe creators should disclose what art is being used in AI training, that AI outputs should not belong to model creators, and express concerns about AI's impact on the art workforce and who profits from their art. We hope the results of this work will further meaningful collaboration and alignment between the art community and Generative AI researchers and developers. 

Subjects: |  Computers and Society (cs.CY)  
---|---  
Cite as: | [arXiv:2401.15497](https://arxiv.org/abs/2401.15497) [cs.CY]  
  | (or  [arXiv:2401.15497v5](https://arxiv.org/abs/2401.15497v5) [cs.CY] for this version)   
  |  <https://doi.org/10.48550/arXiv.2401.15497> Focus to learn more arXiv-issued DOI via DataCite  
  
## Submission history

From: Juniper Lovato [[view email](/show-email/c094cca3/2401.15497)]   
**[[v1]](/abs/2401.15497v1)** Sat, 27 Jan 2024 20:22:46 UTC (10,769 KB)  
**[[v2]](/abs/2401.15497v2)** Tue, 30 Jan 2024 19:59:18 UTC (10,769 KB)  
**[[v3]](/abs/2401.15497v3)** Tue, 6 Feb 2024 17:29:58 UTC (10,768 KB)  
**[[v4]](/abs/2401.15497v4)** Tue, 14 May 2024 23:09:13 UTC (10,748 KB)  
**[v5]** Tue, 1 Oct 2024 12:47:47 UTC (6,631 KB)  


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url: https://www.zhangjingna.com/blog/the-future-of-ai-art-and-automation-in-creative-industries
scrape_date: 2025-03-11T13:08:34.608538
content_type: markdown
hash: fbffecd38dcc1f4f02213ed485fad1a39f1656a4653c1cf632652cb13de59b5e
title:
The Future of AI Art and Automation in Creative Industries - Jingna Zhang  Fashion, Fine Art & Beauty Photography

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#  [ Jingna Zhang ](/)

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    * [Scotland](/scotland)
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    * [A Glimpse at Photo Vogue](/a-glimpse-at-photo-vogue-milan-2013)
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    * [Angel Dreams](/angel-dreams-singapore-2010)

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×

#  [The Future of AI Art and Automation in Creative Industries](/blog/the-future-of-ai-art-and-automation-in-creative-industries)

[Jingna](/blog?author=53adb9c9e4b05e0d792c6c83) December 24, 2022

![](https://images.squarespace-cdn.com/content/v1/53adb9cbe4b05e0d792c6c88/5396eee2-7598-4eb6-bde9-115f88197d11/Team-Liquid-Stacraft2-SC2---Wave-of-One-2022-Campaign---Jingna-Zhang-Art-Direction-Artwork1.jpg)

Art direction for Team Liquid - _A Wave of One_ StarCraft II team photoshoot background.

I’ve anticipated losing my job to AI for 6 years now. I expected style replication and high quality generative art to be accessible by 2021. I’m a year off, but I think it’s close enough. So I want to share what I think is next based on machine learning milestones and behind-the-scenes/B2B tech available to games, photography, and movie productions. 

### A little on my background:

I spent 20 years growing up on digital art platforms, for many of those years, I lived and worked alongside artists in AAA games and Hollywood movies. My photography career has given me insights to production, art direction, and executive-level conversations. My experience in founding an esports team and working at CGHUB also gave me exposure to many different types of founders and AI/ML researchers over the years. 

I’ve seen ML tech offered from 4 years ago that are going to start getting made more public starting now. These are some of my thoughts and observations. 

_(Disclaimer: I’m building_[ _Cara_](https://cara.app) _, a new platform for artists. As we near 6k signups on the waitlist, I want to make sure no one misunderstands what I think will happen to our industry. So I want to be transparent about my thoughts on AI, and why I want this to be more than just an alternative art platform.)_

## My estimates for the near future—

Once AI art’s ethical issues are resolved, and if nothing else changes—I expect that 60-80% of the artist workforce will lose their jobs in the next 2-5 years. Not to prompters—just to company bottom lines.

The most impacted will be mid career and younger artists first, while the higher end jobs and productions will undergo minimal changes. 

That is not to say that there won’t be jobs created. To replace the 60-80% jobs lost, perhaps 15-30% new roles will be made, but individuals would need to fulfill multiple roles by being a good artist, prompter, and editor all at once.

### Percentage seems high? Consider this: 

As an art director, I commission artists. Concept illustrations can take 1-2 weeks, complex pieces 1-3 months. When AI art is ethically sourced and there are no copyright issues, a good artist can use AI to generate hundreds of options of what's needed, then do a paintover to drastically change a piece for delivery. 

Now a 2-week piece would take 4 hours, 3 months becomes 2 days.

This one artist won’t lose their job, but if I needed 10 artists on a project in the past? Now I would only need one.

Where do those 9 people go? Other companies too, will now also have 9 artists they don’t need.

![](https://images.squarespace-cdn.com/content/v1/53adb9cbe4b05e0d792c6c88/51da9821-b7a4-4d15-8220-2d8ed5949424/Motherland-Chronicles-53---Germaine-III---Zhang-Jingna.jpg)

Motherland Chronicles #53 - Germaine III

### How about photography? 

In small ways, AI filters have already decreased the need for some photography services for many. I’ve seen people ask for a photographer and they don’t need the images retouched, because they can use a filter themselves. Others, I've seen people talk about how they don't need to hire photographers because phone apps and filters are so good these days. 

Commercial photography and content creation remains a large portion of photography services however. So let's talk about that. 

Someone like me might not be as affected, it’s true. But there aren’t 200k Vogue covers for 200k photographers out there. Many small and medium businesses won’t care if a photo isn't artistic, if a model’s face is replaced, or if they don’t own copyright to the photos. They only need an image in front of the end customer.

When commercial photoshoots becomes text-to-image—it's not only a photographer who is no longer needed either. There will be no need for a model, makeup artist, hair stylist, stylist, producers, or assistants. Depending on the job, automated commercial studios already exist today where you can strip away many parties in between. 

Need a product shot in environment? Just drop it in. Now you can skip using a set designer, prop stylist, producer, set assistants, and simply produce a product photo in-house and use words for the set you need around it: 

![](https://images.squarespace-cdn.com/content/v1/53adb9cbe4b05e0d792c6c88/5eb830d3-ef3c-47d5-9993-f15c26a57ede/scale-ai-forge-text-to-image-demo.jpg)

Scale Forge: AI-generated product imagery, via [Scale AI](https://scale.com/blog/catalog-forge-early-access)

Need 20 models for diversity? No problem! You can use one face, the tech already exists to turn that into all genders, skin tones, face types, and hair you need. E-commerce companies that shoot daily can save a lot of money this way by hiring just one model instead of 20. 

### More examples? Videos! 

When text-to-video is mature and can replace filming and animation, anything between text and final output is fair game. This can mean camera crew, art department, actors, voice actors, storyboard artists, lighting artists, riggers, animators, technical directors, and more. 

But they can’t copyright AI creations! True and thank god. But as mentioned, high end productions won’t be as affected. What people think of most—Hollywood movies, TV shows, the biggest magazine titles—are unfortunately a very, very small percentage of all creative output in the world.

![](https://images.squarespace-cdn.com/content/v1/53adb9cbe4b05e0d792c6c88/a5f24cd3-bdd8-4ec5-b691-98f1491daa43/A_teddy_bear_painting_a_portrait.gif)

Input: “A teddy bear painting a portrait”, [via Meta AI](https://makeavideo.studio)

Imagine a regular small-medium business that hires multiple people for their social media team. They might need a video content creator, producer, writer, studio manager, talents, editors, assistants, set designer, stylist, etc. But when text-to-video becomes commonplace, you only need a copywriter and a freelance editor to write out the meme videos you want. It's faster, it's cheaper. 

Even right now, companies repost content from creators on social media. It’s only needed as a one-time use content. They just need something new, multiple times a day. The brand doesn’t own the rights and they don’t need to. Copyright exists to protect a creator from having their work stolen and profited off from—but in this case, the company only cares about pushing out content to promote their product. There's no incentive to care if someone else steals their AI generated image or video. They can just make more. 

## Who’s Safe?

The best and biggest names will be safe for a while. AAA studios will keep hiring the best people, magazines that hire me will still hire me (or not), top actors and models are fine. Mid-level animation and video will be safe for a bit longer, because of resources/computing power.

The first to go today are smaller, indie illustrators, retouchers, writers—where the work delivered is standalone and doesn’t interface with other parts of productions, where copyright isn’t important to own for the client, and where potential infringement don’t bother them as much either. So long as it looks good enough, a business will happily prompt it themselves and not hire people for it anymore. 

Book covers, marketing illustrations, poster art, etc, all fall under that. A nice pattern on the throw pillow or dress in the store? That was someone’s job. 

General retoucher hiring will be reduced, because filters are good enough for many non high end jobs. Copywriters are now skippable, because ChatGPT can replace them in many instances. 

## There Must Be Upsides!

AI art will create an overload of content and we will get more than we could ever dream of consuming. Companies will get to save lots of money by eliminating jobs and roles. Smaller creators and individuals _could_ stand to benefit too—provided we could afford the price. 

Other than AI art that is currently unethically scraped without copyright owners’ consent and will eliminate more jobs than it creates, most complex automation and AI tech in the creative fields are B2B and priced accordingly to serve corporations. They will be expensive, but affordable to companies at high price tags because they are cheaper than humans in the long run. 

In time, there may be enough innovation at scale that even small creatives can benefit. I look forward to the day when AI art and tools are ethically made and equally beneficial to everyone, but it’s an entirely separate issue from the job loss that workers in creative industries will need to experience first. 

Long story short,**AI art & automation will impact more art-related jobs than many realize.** We all need to adapt, that is not a question, but the time will also come for when automation and AI reaches more industries across the entire fabric of society. So I sincerely wish there could be a larger conversation around what would become of all of us when that happens. 

People tend not to care unless something is happening to themselves. But nevertheless, I have some small hopes that some people will contribute to the dialogue.

## What Will Happen to Human-Made Art?

I believe human-made art will survive—on a smaller scale. It will likely become a niche like what handmade goods are for us today in a world of mass production.

But we'll need infrastructure, protection, systems, and support in place. (And I think, art platforms will be a great place to help provide some of that support.) 

## What Should We Do?

There's a whole host of issues to address with regards to AI as it will impact everyone across all fabric of society. But before we can begin to address those topics, not stealing from businesses and individuals should be the utmost basic starting point that everyone agrees upon. 

AI development should benefit humanity, doing it on the backs of stealing from millions of people should not be part of it. 

So sign petitions, write to your representatives, pay attention to discussions, do research. 

Be wary of rage bait content from anyone, from any side. Verify and educate yourself by going to the source instead of taking things at face value. Hear about a great new app? Try it yourself before recommending it to others. Hear about a new law? Read it yourself on the official website before sharing what someone else has paraphrased. 

No matter how far removed you feel that you are from the situation, AI won’t just impact other people, it will likely impact you too. 

### A New Platform

I waited and waited for ArtStation to revise their stance. I don’t know if they don’t care because they think people have nowhere left to go? So I thought, if that’s the case, I will build it.

I want a platform that opts images out of scraping by default, that won't host AI art until datasets are ethically sourced and laws have passed to properly protect artists' work. I don't want a platform to simply be an ArtStation alternative. What I want is a community where artists are prioritized.

I want someplace to exist that can help facilitate discussions, education, and support as we navigate the incoming shifts. I want to make it possible for people to continue to find human artists easily, even in the future as we get more and more AI-generated work in society. 

I don’t have all the answers, but people coming together just might. So I hope that people will continue to have thoughtful conversations and champion for respect and fair treatment for all. 

### Building Cara 

Platform development will take time. For now, we’ve launched a beta of our MVP and you can check it out here: [cara.app](https://cara.app)

We are a small group of volunteers in need of more engineers. If you’d like to help, drop us an email at **team@cara.app** or ping us on [**Discord**](https://discord.gg/VdejWWvc5p) if you’d like to contribute! 

### Edit:

*****[ **Cara**](https://cara.app)’s first MVP opened for public signups after 3 weeks! We worked tirelessly to make it happen. And if you are looking for a new place to chat art, share your work, or just want to check out what we are doing, do [check us out!](https://cara.app/explore)! 🥰✨ 

We are still in beta, so I don’t know if we can be a complete art Twitter alternative, but we’ll do our best to build a platform that can support our community. Follow me on [Cara](https://cara.app/zemotion) for more updates.

**2023 Apr 2** : It’s been 3 months since my post and most of my predictions here have now taken shape as public products or demos. I’ve been asked to write more on the topic, but most of my time has been dedicated to building Cara in lieu of meeting my photobook deadlines, so for the moment, I need to focus on wrapping up my obligation to my publisher while still keep development going. Once that is complete, I will participate in the AI conversations a little more. You can follow my [Twitter](https://twitter.com/zemotion) or [Cara](https://cara.app/zemotion) for some shorter form thoughts for now. Thank you. 

—

Notes: 

  1. Why am I so pessimistic? The year before AlphaGo, I ran into Yann LeCun at a conference and asked about training images in my style. Gist of the answer was that it wasn’t really a question of possibility, just computing power, size of dataset, and money. 

When AlphaGo came around, I was watching. I played Go when I was young—I competed in amateur tournaments, photographed the world’s biggest events, met Lee Sedol and Lee Changho, ran a Go blog, and even lived with some professional players for a time. I had ML and CS friends who did their PhDs and did researched at some of the biggest companies in the world. And none of us—none—thought that Go would be solved so soon—including the AlphaGo team themselves, as can be seen in the AlphaGo documentary! 

To watch the best human player in the world defeated so completely—we thought that wouldn’t happen for another 10 or 20 years. Technology was freaking incredible and I felt that elation and excitement, no question. But my heart was [also crushed](https://www.zhangjingna.com/blog/2024/3/20/lee-sedol-alphago-art-and-go). I paid more attention after that. 

Tech takes time to progress, but most people fail to understand just how fast advancements can happen. 

  2. I feel like I've been in social media hell forever this year. I want to just, get back to posting my work and not be doom and gloom every post because this is really, really, exhausting. 

I'm also sure I'm missing nuances and examples and my ML readers are possibly cringing. It terrifies me that I'd likely be trolled and attacked and I've had enough of that this year already. 

So no need to fight me on estimates if you disagree. It's not like I can see the future, I just extrapolated from what I know. 

Things like artists losing jobs is already a fact. But maybe there will be more protection for human workers across society when people start discussing it more. Or maybe research will escalate because corporations are now more invested than ever. Maybe there will be UBI! Who knows. 

Regardless of what you think, I believe that ignoring what the advancement of what AI means and how it could impact our lives is naive. It’s time to pay attention. Please give it some thought. 




*Edit: When I wrote this post, I wanted to share what I knew of AI development that wasn’t talked about at the time of publication. I also wanted to share my stance and on AI because I had just started building [Cara](https://cara.app), a new platform for artists. I made it as a response to the lack of a similar community space for creatives where the founding team has a clear understanding of industry impact and care for artists, so I figured it was important to share my thoughts and perspectives so that people who are new to Cara can see where I stand. 

To be clear, like many working artists, picking up new technology is part of my job. I understand that tools will increase productivity. But I am against the unethical use of artists’ work in training generative models without our consent. 

The logic to unauthorized usage and licensing is simple: licensing is part of my business, so if someone wants to use my work—be it an artist who wants to use it as reference in a painting, or a company that wants to use it in an internal project—they pay for a license so they can use my work legally. People who don’t believe a work or service is worth paying for shouldn’t use it, it’s as simple as that. 

I want to build a platform that is technologically aware and work on educating and helping our community through what is coming. That is my priority. This post is simply my estimates on what will happen in the future. 

In [Thoughts](/blog/category/Thoughts), [Artificial Intelligence](/blog/category/Artificial+Intelligence) Tags [Cara](/blog/tag/Cara), [Art Platforms](/blog/tag/Art+Platforms), [AI Art](/blog/tag/AI+Art), [Automation](/blog/tag/Automation), [AI Ethics](/blog/tag/AI+Ethics), [AlphaGo](/blog/tag/AlphaGo), [Generative AI](/blog/tag/Generative+AI)

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title: The Impact of AI Art on the Creative Industry | Montreal AI Ethics Institute
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Democratizing AI ethics literacy

# The Impact of AI Art on the Creative Industry

August 6, 2023

![](https://montrealethics.ai/wp-content/uploads/2023/08/DALL·E-2023-08-06-16.34.00-artist-working-on-a-landscape-on-a-canvas-looking-outside-their-window-from-their-workshop-digital-art.png)

![](https://montrealethics.ai/wp-content/uploads/2023/08/roxane.png)

✍️ Column by**Roxane Lapa** , a South African digital artist and designer with over 20 years of experience, and more recently a Youtuber and Art Educator.

[[More resources](https://www.roxanelapa.com/anti-ai-generative-art-resources/) by Roxane Lapa]

* * *

**Overview:** Over the past 20 years, I’ve watched with horror as automation, greed, and exploitation has laid waste to the field of graphic and web design, and now the rise of AI generative art threatens to do even worse to concept artists and illustrators. My video calls for society to focus on what’s important so that AI serves us instead of the other way around.

* * *

## Introduction

I started my career as a graphic, web, and digital designer a little over 2 decades ago, and I’ve steadily watched work opportunities dry up. This started with unskilled template users flooding the market and ramping up exponentially with the advent of Wix and their aggressive marketing for their products that cut out the designer altogether.

Nearly 5 years ago, I started shifting my career towards digital art and illustration and a little further away from design, hoping an even more creative field would be more secure. However, it wasn’t long before the first generative AI programs started popping up. Some of my peers naively saw these very early AI systems as fun tools, but I predicted they would eventually threaten the livelihoods of portrait and concept artists. I didn’t realize how soon that would start happening and how many more jobs were on the line.

Here we sit in 2023, and creatives are finally starting to wake up to the realization that we’re being stolen from and made obsolete by greedy organizations.

## Key Insights

### **The Moment I Realized We Were In Trouble**

We saw the first rudimentary generative AI systems emerging a few years ago. I already knew we were in trouble when I saw a free app that could take a portrait and make a disturbingly good stylized version in one click. I could easily envision how most people would stop paying illustrators for avatars when they could do it themselves for free. I’ve seen this pattern before in my design career. 

The generative AI that left me truly unsettled, though, wasn’t one that made a beautiful end product. It was a website where you could upload two characters, and it would remix them and spit out a bunch of variants. The end results weren’t polished. In fact, some were outright janky, but the sheer creativity of it was alarming. Receiving so many options without engaging your brain for any creative work…well, I thought to myself, “There goes concept art as a career.”

The greater problem is that if concept art isn’t safe, no creative jobs are safe, and if no creative jobs are safe, then no jobs are safe.

### **Why Concept Art is the Pinnacle of Creativity**

Many non-artists are easily wowed by photorealistic art, thinking this must be the apex achievement for creatives…to be able to draw a beetle so perfectly that it looks like you can pick it up off the page or have the model stand next to the painting and not be sure which is the model and which is the painting. Oh, how talented they must be.  

The truth is there’s absolutely no creativity involved in photorealism. It’s simply an analytical skill that anyone can learn, and with enough patience, and fine motor skill, anyone can learn to copy something perfectly.  

On the other end of the spectrum, you get concept artists. Their job is to develop new concepts (ideas) for things.  You get concept artists for characters, environments, and props. They’re the creatives who create all the unique characters in all our favorite video games. They conceptualize what the characters will wear, what vehicles and weapons they use, what monsters they fight, and where it happens. They imagine mini worlds full of things that don’t exist in the real world. This is true creativity.

### **How AI Mimics Human Creativity**

If a concept artist is tasked with coming up with, for example, some sort of frog monster, their job is to create a variety of frog monsters for the art director to give feedback on and choose from. When the concept artist puts pencil to paper (or stylus to tablet), they have already looked at frog anatomy diagrams, observed a myriad of frog species, analyzed the environment in which frogs live, and admired existing monsters that others have conceptualized before them.  These references, along with other disparate real-life experiences, form their visual library; from this, the concept artist remixes and creates original frog monsters never seen before.  

AI does something similar, except that its visual library consists of 5 billion images that were scraped as data without consent. And it produces the concepts in seconds.  

That is bad enough. No human can compete, but it also goes the extra step of producing the concepts in the **exact** art style requested. These art styles were also scraped without consent.  

In a recent Judiciary Committee hearing on AI and copyright (_Editor’s note: read more_[ _here_](https://brief.montrealethics.ai/p/ideological-alignment-3-eras-ml)), beloved concept artist Karla Ortiz pointed out the lunacy that we now have a non-human entity that not only competes directly for the same jobs as concept artists, but it uses the stylistic choices of its human competitors as an extra slap in the face. At this point, the AI developers have added an insult to the injury. 

### **What jobs are on the line?**

All jobs are on the line. If human creativity can be mimicked, how much more easily can analytical and technical jobs be usurped? I will leave that thought with you.

## **Between the lines**

I saw a [tweet](https://twitter.com/KarlreMarks/status/1658028017921261569?s=20) recently from satirist Karl Sharro which read: 

"_Humans doing the hard jobs on minimum wage while the robots write poetry and paint is not the future I wanted_." 

Indeed Karl. indeed…but I won’t sit back and accept it, and I hope you won’t either.  

### **How Can We Fight Back?**

In my video, I talk about how we can support lobbyists and vote intelligently so that we aren’t inadvertently supporting the 4th industrial revolution. I mention several lawsuits that may create a precedent for further litigation. I show how social pressure can be used to create a stigma around job-stealing AI, and I encourage a culture where we support human-made crafts.  If creative jobs aren’t safe, no jobs are safe, so please join me in the resistance.

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#  Professional artists: how much has AI art affected your career? 

[ AI Discussion  ](/r/ArtistLounge/?f=flair_name%3A%22AI%20Discussion%22)

First, sorry for bringing up AI. I hope this will be the last AI thread you will ever see. 

I myself have kept AI art out of my radar, until a news article about AI art popped up in my feed , and I made the mistake of reading the comments. 

Most of the truly pessimistic comments are from budding artists, who are now convinced that Ai has trampled any future career they had in the arts. More experienced artists have either been totally silent on the issue, or are absolutely convinced that AI art will never replace the need for human-made art. (It's not easy to tell whether they actually believe that.) 

As a budding artist, it's easy to feel like you're being outdone by a "robot" when you don't have much experience in the art field to begin with. 

But how do you experienced professionals feel about this? Has your career/gig suffered at all since the release of midjourney and dalle-2? If so, how much? 

Read more 

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Frontiers Publishing Partnerships | Digital art work and AI: a new paradigm for work in the contemporary art sector in China

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ORIGINAL RESEARCH 

Eur. j. cult. manag. policy , 21 February 2024

Volume 14 - 2024 |  [ https://doi.org/10.3389/ejcmp.2024.12470 ](https://doi.org/10.3389/ejcmp.2024.12470)

This article is part of the Special Issue Artificial Intelligence: Cultural Policy, Management, Education, and Research [View all 5 articles](https://www.frontierspartnerships.org/research-topics/76/artificial-intelligence-cultural-policy-management-education-and-research/articles)

# Digital art _work_ and AI: a new paradigm for work in the contemporary art sector in China

[![Emma Duester
](https://loop.frontiersin.org/images/profile/2587116/74)Emma Duester](https://loop.frontiersin.org/people/2587116)*

  * Shanghai Jiao Tong University, Shanghai, China



This paper explores a paradigm shift in work culture in the contemporary art sector due to digital transition and the introduction of AI. New ways of working with AI and digital software are embedded and normalized in everyday Chinese artistic practices. This work includes new forms of creativity and efficiency, yet, simultaneously includes new types of digital labour. This paper conceptualizes this as "digital art _work_ ," which draws attention to the often-overlooked aspects of artists' work, particularly their everyday artistic practices that increasingly include digital software and AI. What is the role and position of the artist in an environment where digital software and AI are becoming more central in artistic creation? How do artists creatively (mis)use AI? What does this paradigm shift in work culture mean for the future of the artist's role and the future of the contemporary art sector? This paper draws on 48 semi-structured interviews with visual artists and arts professionals, including painters, sculptors, mixed-media, and internet artists as well as contemporary art gallery owners, museum project directors, curators, and culture policymakers living and working in China during 2023. The findings show how Chinese artists are mastering AI and opening up new spaces for creativity and how the contemporary art sector in China has already transitioned to a new "digital way" in artistic creation. These findings can help to create policy around AI globally and provide solutions for the sustainability of the artist profession and the future of the contemporary art sector.

## Introduction

This paper explores the paradigm shift in the nature of "creative" work in the contemporary art sector, which is taking place due to digital transition and the introduction of AI. This paper uncovers how artists use AI in combination with other digital software to create ideas, research, exhibition proposals, and as part of their digital art work. This includes investigation into the often-overlooked aspects of artists' work by focusing on their everyday artistic practices that increasingly include digital software and AI. This allows a presentation of the human account in this very much technological determinist global discourse, in terms of how _we_ can create creativity and innovation and growth in the sector through these kinds of uses of AI. This paper considers how artists use AI, how they feel with using AI, and their thoughts about its use going forward for contemporary art sector development, policymaking, and management. This new knowledge can help with future policymaking and the management of AI globally and of ensuring sustainable and effective contemporary art sector development by showing how these artists are harnessing AI for creativity--showing opportunities of AI from artists' perspectives and showing the impacts of AI on the future of work in this sector.

There has been rapid digital transition in China, resulting in a mature and integrated digital ecosystem today, bringing opportunities for cultural development and digital access to culture. However, artists must keep up with this trend. The unique situation in China can demonstrate what is happening in the contemporary art sector globally as a result of rapid digital transformation, particularly with new uses of AI, and provides a case to show the opportunities and changes AI can have on contemporary visual artists' practices and the contemporary art sector. Artificial Intelligence (AI) is now a common tool for the new generation of contemporary visual artists in China. AI programs such as Stable Diffusion, DALL-E, MidJourney, and ChatGPT have spread rapidly in the contemporary art sector in China during 2023 due to the mature, advanced digital environment and a general appetite amongst artists for convenience and accuracy in their work practices. This opens-up the discussion of whether AI can be creative and empowering in work. This provides a novel angle amidst existing discourse that takes a predominantly critical standpoint towards AI concerning copyright, morality, and ethics (Tajalli, 2021; Appel et al., 2023) or AI as jeopardizing creativity (Bisoyi, 2022; Nolan, 2022). The aim in this paper is to connect these two sets of literature on precarious creative work that discusses "freelance," "precarious," or "passion but precarious" working conditions (Sigler, 2017; Serafini and Banks, 2020) and on digital aesthetics and techniques (Astle and Muir, 2002; Kucuk and Soydal, 2003; Yilmaz and Celic, 2011) in order to raise awareness on and increase the amount of literature that connects the two in order to show the impact of AI on working cultures.

Focusing on the contemporary art sector and visual artists in China can provide new perspectives on AI, by showing how AI can be positive for artists' work by enhancing their productivity, efficiency, and innovativeness. It can also show new ways of using AI while still maintaining human control and creativity. This unique situation/case can be used to show what is needed in terms of cultural management and policymaking for sustainable and effective culture sector development through this historical period of AI adoption. As a result, this research expands a small set of literature that sees the positives of AI for artists' work (Mitchell et al., 2003; Edmonds et al., 2005) and fills a gap in the literature about this in relation to China. With much discussion about and use of AI from companies, businesses, and the tech sector, it is evident there is a need to hear from artists who are often vanguards with new technologies. Hence, artists can shed light on nascent bottom-up forms of creativity in using AI. This research shows artists as a significant and valuable voice who are able to speak directly to the changing and nuanced questions currently taking place globally--through discussions by those who are using data, machine learning (ML), and AI everyday in their work. For example, artists have historically deployed new technologies in unexpected and often prescient ways (Rieland, 2014; Shanken, 2022) and have been interpreted as vanguards of new ideas, techniques, and cultural practices (Francastel, 2000).

"Digital art _work_ " is conceptualized in this paper as a way to understand the paradigm shift in work cultures in the contemporary art sector and, in particular, artists' digital work practices. This is a play on words, meaning both artists' artworks as well as their artistic practices that belie a particular politics of everyday digital labour. This term allows for a broader discussion and definition of art work, beyond only the final artworks' aesthetics and technical aspects and on "digital art" that discusses digital techniques or digital aesthetics (Astle and Muir, 2002; Kucuk and Soydal, 2003; Yilmaz and Celic, 2011) and digital techniques and digital aesthetics of AI art (Manovich, 2018; Contreras-Koterbay, 2019; Liang, 2022). Instead, this paper argues more discussion is required on the human, social issues of sustainability of the contemporary art sector, artists' role, and considerations of how artists creatively (mis)use AI. This is required because AI is not only influencing artworks but is also influencing artists' working conditions. However, the latter is not yet covered in the literature on AI and art or AI and the creative industries. Hence, more connection between AI and changes in working conditions is needed.

This study includes 48 interviews altogether, comprising 25 in-person interviews, 20 online interviews, 3 written interviews. Please refer to the Supplementary Appendix for a full list of all interviewees' positions, ages, and gender. There were 32 female interviewees and 16 male interviewees. This study includes 29 interviews with contemporary visual artists, including painters, video artists, multi-media artists, sculptors, internet artists, and photographers, who work within the contemporary art sector in China. This research used the following definition of "artist"--as a person who creates art (such as painting, sculpture, music, or writing) using conscious skill and creative imagination, having graduated from an art academy or university in art related major. This paper also refers to "creative workers" in order to combine artists' and arts professional's work, to emphasize aspects of their daily work, and to show they are also exposed to labour demands. This paper focuses on independent artists and their studio work practices. The artists are aged between 20 and 61. This wide age range was chosen to gain a broader perspective on use of digital technologies and AI. These include internet and metaverse artists such as CHILLCHILL, Wang Xin, and Shi Zheng. There are also other young artists who are sculpture, video, and installation artists but who have very digital orientated work processes, such as Armida, Yan, and Maggie. Each interviewee is introduced in the Endnotes. All interviewees included in this paper have given written consent to share their name and biography.

The study also interviewed 19 arts professionals from across China, including curators, contemporary museum professionals, contemporary art gallery directors, contemporary art space project managers, and cultural policymakers. The interviewees are from a mixture of government, independent, and private organizations within the contemporary art sector. However, this second set of interviews with arts professionals are for the purposes of providing broader context and understanding on the overview of what is happening in the contemporary art sector and how the sector is being influenced by new digital technologies such as AI. The interviews were carried out during 2023, either in person or online. Participants are of different ages ranging from 27 to 59 years old. This wide age range was chosen to gain a broader perspective on use of digital technologies and AI in particular. The Results section includes some perspectives from contemporary curators discuss the topic of AI and new digital technologies in their exhibitions or who use AI in their exhibition design process, such as Wang Xiquan and Jenny.

This is coupled with a systematic review and analysis of government policy documents on national digitization strategy, national cultural development directives, contemporary art sector policies, (digital) cultural policy, AI policy, and directives for the integration of culture and technology. Analysis of interviews was based on these same theme of digital work culture, changing work practices in the contemporary art sector, how culture and technology have been integrated in artworks and creative workers' entire working practices, and how the "culture" of work in the sector overall is changing. Interview analysis was conducted to see how national government policy has impacted the sector in actuality.

The participants are all trained arts professionals or artists, having graduated from an art school, academy, or university. They define themselves as either artist, "cultural worker," digital artist, or painter. They include different types of artists like sculptors, painters, digital artists, video artists, new media artists, and mixed-media installation artists. In recruiting stage, they did not need to have any previous experience with technologies. Subsequently, the sample includes a wide range of artists and cultural professionals to understand the broader perspectives on use of AI and nature of artists' work today.

## AI adoption and regulation in China

In a relatively short period of time, China has become one of the world's most technologically advanced nations. Due to a streamlined and mature digital infrastructure, AI has spread rapidly and is now being used in many creative industry sectors including art, music, and film. This is being funded through large-scale collaborations with big brands and tech companies. AI has also developed rapidly due to government backing and the increasing value the government places in culture, technology, and innovation. Policy efforts on digital transformation in China have been progressive and forward-thinking, clearly showing its investment in the creative industries and the digital cultural economy. This is reflected in the government's long-term strategy for economic and national development (Montgomery and Keane, 2004). The 14th Five-Year Plan for Cultural Development states that "it is necessary to further develop and expand the cultural industry, and give full play to the role of culture in activating development momentum, and promoting the optimization and upgrading of economic structure" and, particularly, to promote the "integration of culture, science, and technology" (CHINESE GOVERNMENT, 2022). In addition, the Chinese government is now developing the digital creative industries for economic growth and national development goals (Zhang Jie, China [Daily.com](http://Daily.com), 8th September 2022).

The creative industries, particularly the contemporary art sector, have been rapidly and radically combined with digital technology over the past 7 years. Related policies for the development of digital technology into the contemporary art sector were introduced in 2016. In 2023, a national digitization strategy was outlined towards 2025 to promote the integration of new digital technologies into various industries, including the contemporary art sector with "the integration of art and technology" (CHINESE GOVERNMENT, 2021). As a direct result of these policies, many artists are now creating digital artworks and using more digital processes. These methods are now preferred by many and have replaced the traditional artistic creation process. This means that AI is influencing artwork aesthetics and techniques _and_ the working conditions of artists. The working conditions in the contemporary art sector in China today are characterized by self-employment along with small- and micro- art enterprises. Artists and arts professionals tend to have project-based careers and a high degree of mobility, they often have an irregular and unpredictable income, and they combine several jobs to earn a living. The fully online conditions during the COVID-19 pandemic accelerated developments in digital technologies but has also normalized these digital working conditions. Hence, this unique situation can show other countries and regions what working conditions after rapid digital transition and the adoption of AI looks like in the contemporary art sector.

In particular, China is ahead of many countries in the adoption of AI. The Chinese Government began supporting the AI industry at the national level in 2016. For instance, the 13th Five-Year Plan (2016-2020) specified AI as key for achieving economic growth and the 14th Five-Year Plan (2021-2025) outlined a plan for continued state investment in AI. In 2017, the Chinese Government introduced a vision for the development of AI in the Next Generation Artificial Intelligence Development Plan (新一代人工智能发展规划) (NGAIDP). The Plan outlined China's strategy of using AI for socio-economic development and creating an AI industry that would position China as the world leader in AI by 2030, and to emerge as the global leader in defining ethical norms and standards for AI. China wanted to make AI "the main driving force for China's industrial upgrading and economic transformation" (CHINESE GOVERNMENT, 2017). The NGAIDP is the first national-level legislation that focuses on the development of AI as a unified strategy.

From 2020 until 2022, several AI laws have been published at various levels in China in response to these broader strategies. At the regional level, China's first provincial law on AI development took effect on 1 October 2020, with the publication of the [Shanghai Regulations on Promoting the Development of the AI Industry](https://www.ssme.sh.gov.cn/public/news!loadNewsDetail.do?id=2c91c28d83647d7001837da955a60a55), which sought to promote the AI industry at the municipal level in Shanghai. Also, the Shenzhen government passed a similar law on the Regulations on Promoting Artificial Intelligence Industry in Shenzhen, which took effect on 1 November 2022. At this time, Beijing also started outlining policies for specifically addressing generative AI systems. The [Internet Information Service Algorithmic Recommendation Management Provisions](http://www.cac.gov.cn/2022-01/04/c_1642894606364259.htm), which set up a governance framework for regulating recommendation systems, came into effect on 1 March 2022. In addition, Beijing released more targeted rules for generative AI through the [Internet Service Deep Synthesis Management Provisions](https://www.gov.cn/zhengce/zhengceku/2022-12/12/content_5731431.htm), which came into effect on 10 January 2023, which applies particularly to deepfake outputs from AI technology. In 2023, China's central and local governments have issued AI-related policies, wherein the "artificial intelligence industry," "application of major artificial intelligence scenarios," "standard system," and a "technical security system" have become policy priorities (CHINESE GOVERNMENT, 2023a; CHINESE GOVERNMENT, 2023b).

In 2023, the "First National Artificial Intelligence Application Scenario Innovation Challenge" was officially launched (CHINESE GOVERNMENT, 2023b). This competition is jointly sponsored by the Chinese Society of Artificial Intelligence and the New Generation Artificial Intelligence Development Research Center of the Ministry of Science and Technology, with the theme of "Scenario-driven Digital Intelligence Power," aiming to drive key core technologies of artificial intelligence with scenario innovation, focus on solving major application and industrialization problems of artificial intelligence, promote the deep integration of artificial intelligence and the real economy, and contribute to the transformation and upgrading of China's digital technology and the high-quality development of the economy (CHINESE GOVERNMENT, 2023b). These policies are guided by the report of the 20th National Congress of Xi Jinping in 2022, which outlined the implementation of the innovation-driven development strategy that aimed to strengthen the national strategic scientific and technological strength (Xinhua News Agency, 2022).

On 13 July 2023, the Chinese government published its finalized rules on generative artificial intelligence, the Interim Measures for the Management of Generative Artificial Intelligence Services (CHINESE GOVERNMENT, 2023a) which came into effect on 15 August 2023. The objective of the Interim Measures is to regulate generative AI, which is primarily designed to generate content and promote the use of AI for national economic development, ease of use, and to promote business and individuals' innovation. This makes it one of the first countries to regulate the technology to _promote_ its use.

These changes in policy are happening as policymakers globally carefully study the introduction of generative AI tools such as ChatGPT and increasingly view their regulation as a policy priority. In other parts of the world, such as the European Union and the US, governments are slowing developments and applications of AI with discussions on ethics and morals surrounding AI, copyright, ownership, and the artist's role. Subsequently, regulations in the EU are outlined to prevent the risks of using AI, while the regulations in the US are for ensuring more accountability and responsible use of AI. As the World Economic Forum (2022) states, "the need to resolve issues around the Responsible Use of Artificial Intelligence (AI) has become increasingly important for countries, citizens, and businesses over the last 8 years, with approximately 60 countries now having National AI Strategies and many have, or are creating, policies which allow for responsible use of this technology." That said, a survey by Adobe (2018) showed that three-quarters of visual artists in the United States, United Kingdom, Germany, and Japan would consider using AI as assistants, in areas such as image search, editing, and other "non-creative" tasks. As Anantrasirichai and Bull (2022) argue, this indicates a general acceptance of AI as a tool across the contemporary art sector globally.

## Impacts of AI on art, the art industry, and work

This paper intervenes in literature on digital transformation in the contemporary art sector and trialing new technologies like AI in art. There is a lot of focus in the literature on digital techniques and digital aesthetics (Astle and Muir, 2002; Kucuk and Soydal, 2003; Yilmaz and Celic, 2011). 9 In particular, some scholars discuss the artists relationship with AI and levels of control in this collaboration. In fact, some scholars understand AI as a favored collaborator in artistic creation. As Miller (2019) argues, "recent advancements in deep learning have reinvigorated interest at the intersection of art, creativity and machine learning." This literature is concerned with whether it is possible to control these digital technologies. As Lopez (2023) argues, "AI has emerged as a desirable collaborator in artistic creation. Artists use AI as a creative tool and work with algorithms to set up specific rules […]It is the artist who will train the AI and determine its environment and learning rules, which makes the AI dependent on the artist" (Lopez, 2023). Hence, there are some who say the artist can control AI by determining the environment, rules, and input data. There is also a small set of literature on the empowering nature of new digital technologies for artists' work practices, although, this has not yet been applied to AI. For example, Edmonds et al. (2005) discuss how "the arrival of digital media and computational tools has opened up new possibilities for creative practice" and Mitchell et al. (2003) state that "the connection between technology and creative practice has yielded many economic benefits and cultural value," arguing that technology can increase creatives' productivity levels.

However, there are concerns about how digital image-making technologies are fundamentally changing understandings of art (Kittler, 1999; Kholeif, 2018; Lopez, 2023). These claims suggest that the "digital condition" induces a loss of trust in the image and a blurring of boundaries between artist and audience (Kittler, 1999). There is also a set of literature that discusses the changes in artworks (Kittler, 1999; Towse and Hanke, 2013; Douglas, 2015) due to digital technologies and how the fundamental meaning of and way of seeing art is changing due to new digital technologies such as AI. For example, Kholeif, (2018) argues that the viewer now sees artworks differently through digital technologies. Kholeif, (2018) argues that a new cultural and artistic landscape has taken shape since 2000, "the post-digital condition," since when there is a new language for seeing, feeling, and experiencing art. Also, BRIDLE, (2014) argues "the new aesthetic refers to the increasing appearance of the visual language of digital technology and the internet in the physical world, and the blending of the virtual and physical." There is a small set of literature about artists' uses of AI in artworks (Mitchell et al., 2003; Edmonds et al., 2005; Miller, 2019; Lopez, 2023), which focuses on aesthetics and techniques. However, this requires discussion about how artists feel about working with AI and how they creatively (mis)use AI to maintain their control and authorship.

This paper also intervenes in another set of literature on the nature of work in the creative industries, which focuses on flexible, freelance, and "passion but precarious" working conditions (Serafini and Banks, 2020) and the totalizing nature of their work that leads to a blurring between work and life (Sigler, 2017). This relates to literature on the nature of creative vis-vis demanding artistic labour since the development of web 2.0 (Towse and Hanke, 2013; Douglas, 2015; Sigler, 2017), which hints at ho the nature of work has become more about multi-tasking and pressured with online work. For example, Douglas, (2015) argues "technology and the internet inherently permeate the work of contemporary artists," McHUGH, (2014) argues "you're always on, always having to manage your online presence."Kholeif (2018) mentions how today "artists are working in diverse contexts, from eBay auctions to augmented reality." Troemel (2014) also argues "the divide between artist and viewer becomes negligible when users of social media are able to more powerfully define the context (and thus the meaning) of an artwork" and "artists on the internet need an audience to create for." However, this set of literature on artists' work has not yet been updated to include the changes in work conditions due to the introduction of AI.

## Digital work as empowering

New digital skills are needed today, which require constant learning and often rapid and radical adaptation to new digital updates. This increase in "digital work" was accelerated by the COVID-19 pandemic as, during this time, a lot more content and practices shifted online. However, it was already very much established in their practices beforehand. As Wang Yiquan1 says, "In the past 2-3 years, I have learnt more about and used more digital technologies, including AI. Because of covid the physical path was not possible so there was more online."

Digital technologies are difficult to operate and, hence, consume more work in order to learn and master. However, many artists in the current generation are unprepared because they have not studied these digital skills at art academy or university. Hence, many spend hours learning, testing, and trialing before they start their actual artistic creation. However, they seem to be of the mindset that you should have a constant state of learning and be able to easily adapt to new technologies. Indeed, Shi Zheng2 likes to have this feeling of newness with her projects and it has now become normal to learn a new technologies and systems for each new project. As Shi Zheng says,

"digital technology is definitely more convenient … But using it I think is something that takes time to learn … Technology change is actually always there, and because I use different technologies for each project, there will be a learning phase to constantly learn and use a system inside each work. It may add some freshness. I think for the project being done to maintain a state of learning, rather than a technology is used for a long time after the very skilled to produce. I may have such a habit."

AI is used to help in this learning process. As Wang Xin3 says,

I have spent a lot of time self-learning those things [coding, AI] and self-taught myself how to use this as my tool to help my artworks. I have to update myself, and because I use it everyday it is easy to update. Always learning. Actually I enjoy. AI can help me to learn now. Like ChatGPT is really helpful to fix the coding part. I write coding and sometimes it's not right, so I copy and paste to ChatGPT and it fixes the code. Actually I use ChatGPT almost everyday to help me fix my code. Maybe before I spent several days to fix but now they can do it in several second. So it really helped to improve my progress.

Some artists express how they are empowered by using AI. Some feel AI gives them power, stimulation, creativity, enhances their mental and imaginative capabilities, and improving their accuracy and progress. Miao Xiaochun4 says, "the new technology gives me four hands." They feel AI gives them more power, speed, and time. As Yan Xiaojing5 says, "digital is good for accuracy and allows me to compute large amount of information in little time." Techniques or ideas that used to be impossible are now possible with new technology. Hence, artists say that they can think of doing whole new things and it expands their imagination.

Only two artists were worried about losing their jobs to AI, while the majority see AI and digital software as "tools," "thinking systems," "stimulants," "another pair of hands" to enhance their performance. The majority are comfortable using AI for creating their ideas, images, and producing artworks and are not openly worried about losing creativity or their position as an artist.

## Digital artistic creation processes

87.5% of participants use AI, regardless of their age or art medium and including both artists and arts professionals. The most popular AI software are MidJourney, DALL-E, Stable Diffusion, and ChatGPT. 18 said that they have used AI for testing ideas, 2 use AI for research, 4 use AI for idea creation, 3 use AI for image creation, 5 use it as a research tool, and 3 use it for text writing for the final piece (particularly video artists), and 7 have used AI in the final visual piece or exhibition. For example, some use DALL*E as it is easy to use but they notice it is not as powerful as MidJourney. Some use AI generated images for inspiration while others use them for their art creation process and others use them for the final artwork. Some use ChatGPT to create poems or scripts for final artworks, either by inputting their own question or an existing text into ChatGPT and waiting for it to generate text.

"I use AI and ChatGPT for research, to find out answers to my questions. It makes me feel less lonely in the creation process[…]I use machine learning for idea creation, and I use rendering and modelling for visualizing. And I use Blender and some mixed with Photoshop for generating the exhibition view." (Maggie)

AI has a clear purpose and role in artists' work processes, either to help with idea creation, research, helping to create scripts, or as a central part of the final artwork. Hence, these artists' work increasingly revolves around using a combination of AI, machine learning, and digital software from idea stage to factory production stage. AI is used as part of the creation process and in combination with other digital software. They have gone past the stage of testing AI; AI is very much integrated into their artistic creation process. AI is most commonly used in the initial stage of artistic creation, before using digital rendering, modelling, or game engine design software. As Blair6 says, "Today, they can make the whole piece from digital. Digital-born art. More young artists are doing that. It is very new."

As Wang Yiquan says, "because AI changes the image and the ecology of the image a lot and how we can make an image, the whole creation process has been subverted." AI has its own distinct position and role in this digital artistic creation process. As CHILLCHILL7 says, "I think AI has its own beauty, aesthetic and style, totally different than 3D software and rendering."

These artists apply this digital work process for the creation of artworks because they see it as efficient, cost-effective, and practical. As Armida8 says, "It's convenience, such as ChatGPT, it's really beneficial to the creation of your work." Maggie9 says "more people are doing it digitally in China, just because the production side of it is more approachable. With factories. Cheaper, more efficient, quicker."

Artists can complete more work in a shorter amount of time with the aid of AI. This makes them feel they can achieve more, move forward quicker with ideas, and be more efficient and productive in their work. As Wang Yiquan says, "it moves my ideas forward." This brings work satisfaction because the digital way makes them feel more accomplished. CHILLCHILL states the practical reasons for moving from being a painter to internet artist:

"I switched to 3D animation, it gives me more motion, and painting takes too much time. If I use the computer I can do it quickly, so it's more convenient, so I switched to the digital way. Painting needs a big studio with a high roof, you need to live outside of the city, but I like to live inside the city, so like this studio, it has to be small, too small for painting."

This total turn to "the digital way" is germane to the culture and recent history of rapid digital transformation in China. The distinctiveness of China's technological development and its influences on the way artists work is shared by participants:

"In China now the digital things develop so rapidly and all the designing ways and techniques change in recent years so we need to study and create many new things with new rules and online tools." (Santy)10

"In China, it's about how perfect you can do things, and then we have the technology, which can do better, so why not let the tech do things. This is many people's mindset. But if you want to be creative, you can get trapped with what the dig tech can allow you to do." (Jenny)

As a result, the role of the artist is changing, as they collaborate now with a digital suite and are always changing or updating their skills. This means that, as Wang Yiquan says, "the lone-scholar-artist tradition in China is changing." The scholar of one material is not favoured by this generation of artists, who are always learning new systems for every project.

## Artists' creative (mis)uses of AI and digital software

Some artists discuss how they creatively misuse AI. They are also using AI and other digital software in novel ways and not in the ways the technology was intended to be used. Importantly, all are using AI in combination with other digital software, including Blender, Unreal Engine, Unity, Miya, and Premier Pro. They are combining software, mixing mediums such as video and game, or purposefully misusing the software. Participants use words to describe this creative control they have found, using words such as "training," "controlling," "mixing," "breaking" and "influencing." They say they do this to get creative results. CHILLCHILL says "I don't like to use the software in the correct way. I don't want to use it correctly. I want to mess around and do something new. Definitely, this way can be creative." Also, Shi Zheng says "I break the system to use it well. I use a different technologies for each artwork to get good results […]I try to think of other ways to influence it [the AI]."

Some artists are combining software for creative effect. As CHILLCHILL says,

"I want to mix them together [the software] to have something fun out of it. I just want to play with it, and see what I can get from it. I started using ChatGPT this year, Mid Journey and AI stuff. I am mixing them together[…]I get more and more freedom because now I can add more things inside and it changed my work a lot, made more freedom. There is more possibility than still image. The creating part with AI and software is more fun to me, because I can combine image, video, music, and then it's like the whole performance. I really get used to using dig technologies."

Many are purposefully not using the software in the correct way for creative effect. Instead, they want to "mess" and "play" around and do something new. As Yan Zhou11 says,

"right now I'm working on my new work using Unity software that I use a lot, back in 2012 its totally different because if you know about lots of video artists they are doing the shooting they are using the camera to do the shooting and putting the footage into the software like FinalCut or PremierPro. So what I do is kind of different. Because I use this software _a lot_ so right now I am doing something really experimental--I am using this real-time, this is prof game design software--but I use them as a video editing software."

## Discussion

These artists consciously use AI while still being mindful to maintain control over their creation process. This allows them to find space for creativity, originality, and authorship. Creativity, originality, and authorship come from mixing AI with digital software, misusing these technologies, or mixing mediums. Hence, they have changed the type of or way of producing and ensuring creativity, which is now comes in the form of mixing, combining, and misusing. However, this is only possible with the mastery of this digital technology. Nevertheless, many see AI as a tool they can control. They find ways of controlling AI so that they can maintain their positions as artists.

New digital software is seamlessly integrated into the artistic creation process because there was already an established digital process in place in China. AI is the latest edition to their suite of digital technologies for creation. This is the case for many types of artists, not only internet or mixed-media artists. Painters, sculptors, and installation artists also now use the same "digital way" and so also become "digital artists" or at least have become accustomed to digital art _work_. This can expand the meaning and definition of "digital artist" and shows what working conditions are like in an digital advanced environment. Importantly these artists seem to have moved well beyond only testing AI, as it is now well established in their daily work lives.

The increasingly close collaboration with AI is changing the artists' role in the creation process. There is less physical labour involved in terms of playing with and creating physical materials. Subsequently, these artists have become a director or manager, using a suite of digital software as their team. Even though this is creating efficiency and convenience as participants say, the findings show how the increasing presence and reliance on new digital technologies places new labour demands on these artists. They believe that they should have the ability to learn this new technology and feel that those who can do this will have an advantage today. This learning of new technologies and accruing new digital skills has been felt acutely and as largely normalized across industry sectors and in society in China due to recent rapid and radical adaptions to new digital technologies.

## Conclusion

Currently, AI is being spoken about internationally in isolation and about what it can(not) do and its negative impacts on creatives and industries. However, the example of China can show an international audience how it is also beneficial to understand how people are using AI in creative combination or mixture with other digital software or using it in the wrong way for creativity and innovation and understand how artists are themselves pushing and developing the technology for their own ends. This can also show how _humans_ can control this technology, how _humans_ can generate creativity and innovation in collaboration with AI, maintain the position of the human artist, and create growth in the sector through these kinds of uses of AI. In fact, AI can make artists more creative and empower them as it can help them to move forward with their ideas faster and more accurately. Unlike many other countries that are still testing and trialling AI, these Chinese artists have mastered the technology and have found a space for themselves to be creative, experimental, and still be an artist within the technology. Forward-looking government policies on the integration of culture and technology have shown in this paper to have a direct impact and influence on how and how much artists and other cultural workers are using AI and dig software in their practices. This is also important to share with an international audience.

This paper has provided new ways to illuminate and better understand the platform-mixing and creative (mis)uses of digital technologies, including AI, in artists' creative process. It has illuminated bottom-up initiatives and ways of using AI. Hence, these findings provide an alternative perspective to concerns that tech companies have control in developing the tech and are moral arbiters of how you can use the digital technologies (Ganesh and Moss, 2022; Ross et al., 2022) and concerns around the underlying digital systems used by artists that dictate how they create, store, and present work. In fact, these Chinese artists are breaking the ways the software was designed to create works that reflect their own authorship.

Furthermore, a different set of considerations have emerged through speaking with contemporary artists, compared with from the IT sector, academic sector, or business sector, because artists are working independent from the bottom-up. This has also provided the human side in this mainly technological/business/commercial/company-based global discussion. With this new knowledge, considerations can be made for the future of artists' jobs, roles, and skillsets amidst digital transition. This can be applied in the future to ensure sustainability of artists' practices, creativity, and jobs and to maintain value in the art sector going forward. Digital technologies can be used for development; however, there is a need to know the reality of creatives' work and their first-hand, in the moment, perspectives on these digital technologies, to be able to understand how the art sector can sustainably go forward in the future.

This paper has shown how AI is changing the contemporary art sector and how it might impact the direction and nature of the contemporary art sector in the future. The paper has specifically addressed independent artists and their studio work practices in this shift in the industry. AI is discussed as taking over human creativity … but this paper shows it has changed working conditions in terms of making them more efficient, accurate, convenient, and allows them to move forward with their ideas more quickly. Hence, AI can serve as a facilitator for more efficient working conditions. In order to ensure sustainable jobs for artists and the development of the contemporary art sector, the following should be applied. We should set standards for best practices in using AI in art that can be applied in the future to ensure sustainability of artists' practices, creativity, and jobs and to maintain value in the art sector going forward. We need to understand and respect how artists are mixing AI with other software for creativity, mixing different mediums like game and video with art to make it creative but also to ensure their position is sustainable. If we want to respect and value this new medium long-term and for sustainable longevity use, then cultural managers and policymakers need to ensure the value of this new medium and recognize this medium as a carrier of culture in itself. There should be policy to ensure that artists' time can be used for artistic creation. The precariousness of continual learning and unpaid digital labour in learning new software should be regulated. There needs to be a clear cultural policy and management strategy to outline standards and protocol for regular training for independent artists.

This paper connected two sets of literature on precarious creative work (Sigler, 2017; Serafini and Banks, 2020) and on digital aesthetics and techniques (Astle and Muir, 2002; Kucuk and Soydal, 2003; Yilmaz and Celic, 2011; Manovich, 2018; Contreras-Koterbay, 2019; Liang, 2022) in order to raise awareness on and increase the amount of literature on the impact of AI on working cultures. As a result, this paper has been able to more concretely demonstrate how "digital art _work_ " is much broader than previously conceived in the literature as only digital artworks, the digital aesthetics applied, and digital techniques of the artworks (Astle and Muir, 2002; Kucuk and Soydal, 2003; Yilmaz and Celic, 2011; Manovich, 2018; Contreras-Koterbay, 2019; Liang, 2022). This has been achieved by showing digitality in artists' everyday work practices rather than only final artworks. This paper has also helped update literature on the nature of work in the contemporary art sector and has also progressed this literature by exposing the shifts in work practices that have taken place due to the introduction of AI. There is now a digital working culture that includes more efficiency and innovation as well as new digital labour demands. Rather, existing scholarship has a lot to do with creative labour as "freelance," "precarious," or "passion but precarious" working conditions (Serafini and Banks, 2020) and the totalizing nature of their work that leads to a blurring between work and life (Sigler, 2017). However, this paper has updated these conceptions of "creative work" and "creative workers" by showing the nature of digital work, the digital nature of contemporary visual artists' artistic creation process, and contemporary visual artists' feelings about AI and digital software. Moreover, this paper has provided a new perspective on this topic from China today rather than existing literature that is more commonly about the West and Global North. Hence, this paper has updated and expanded literature on digital techniques and aesthetics and the opportunities of AI to provide more efficiency at work by talking to contemporary visual artists.

## Data availability statement

The datasets presented in this article are not readily available because they are secure and private. Requests to access the datasets should be directed to ZW1tYWR1ZXN0ZXJAc2p0dS5lZHUuY24=.

## Ethics statement

The studies involving humans were approved by the USC-SJTU Institute of Cultural and Creative Industry, Shanghai Jiao Tong University Ethics Committee Board. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided verbal or written informed consent to participate in this study. Permission was also gained for use of specific quotes from participants that are used in this paper. Participants included in this paper have also agreed to be identified and have provided their names.

## Author contributions

The author confirms being the sole contributor of this work and has approved it for publication.

## Conflict of interest

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

## Acknowledgments

This paper has been presented at the European Network of Cultural Management and Policy Congress (ENCATC) 2023.

## Supplementary material

The Supplementary Material for this article can be found online at: [https://www.frontierspartnerships.org/articles/10.3389/ejcmp.2024.12470/full#supplementary-material](https://www.frontierspartnerships.org/articles/10.3389/ejcmp.2024.12470/full#supplementary-material)

## Footnotes

1Wang Yiquan (b. 1987) is a curator and designer based in Shanghai. He is one of the founding partners of Acts and Pathways, which is a design company that he established in 2018 with designer Wu Jiayin. His research interests as a curator focus on the relationship between the city and art as well as the relationship between art and the economy. He has also contributed to a wide range of urban spatial designs and research projects in Shanghai, Beijing, and Hangzhou. Wang Yiquan received a BA Degree in Journalism from the Beijing International Studies University. He also studied Visual Communication Design at the Central Academy of Fine Arts in Beijing and completed an MA Degree in Narrative Environments at Central Saint Martins in London. Yiquan is originally from Beijing and now lives and works in Shanghai.

2Shi Zheng (b. 1990) is a contemporary visual artist based in Shanghai and New York. Shi's artworks include audio-visual installations, digital music, and live performance. Shi is interested in virtual simulation, "machine vision," and the philosophy of technology. Alongside his individual artistic creations, Shi also collaborates with other artists in various fields. For instance, he works as part of an artist group called RMBit that was founded in 2013 by Shi and Nenghuo, Wang Zhipeng, and Weng Wei. This artist group focuses on the current context of social media. Additionally, Shi is part of the Audio-Visual performance group called Open Super Control (OSC). Shi's individual and group works have been exhibited at museums, art institutions, and media art festivals in China and internationally. Some of these institutions include TANK Shanghai, Sifang Art Museum, MOCA Yinchuan, Sound Art China, Beijing Biennial, FILE Electronic Language International Festival, Ars Electronica, Institute of Contemporary Arts London, and Castello di Rivara. Shi received a BA Degree from China Academy of Art and received an MFA Degree from the School of the Art Institute in Chicago.

3Wang Xin (b. 1983) is an artist who is based in Shanghai. Her work focuses on the topics of the status of the artist, the functioning of the art market, and the use of AI for hypnosis therapy. In 2016, Xin had a solo show at the de Sarthe Gallery, Hong Kong, titled "Every Artist Should Have A Solo Show". Some of her works include commentary about her own position and relation to the art world with the use of a sense of humor. Her second solo show, titled "The Must-See Art Show Where You Can Find 10,000 Artists", exhibited at de Sarthe Gallery in Hong Kong, focuses on the functioning of the art world. Both exhibitions include interactive, site-specific installations. Wang is also a certified hypnotist and has explored using hypnosis in her art with the use of AI. Wang received a BFA Degree from China Academy of Art in 2007 and received an MFA Degree from the Art Institute of Chicago in 2011. Wang is from Yichang, Hubei, and currently lives and works in Shanghai.

4Miao Xiaochun (b. 1964) is a new media artist who is based in Beijing. Miao is known for his large-scale photographs and panoramas of modern Chinese cityscapes. His artworks include computer-generated installations including one notable artwork titled "The Last Judgement in Cyberspace". This is a 3D monochrome reworking of Michelangelo's "The Last Judgment" from the Sistine Chapel, whereby Miao has replaced every figure with a virtual model of himself. He began exploring the connection between the real and the virtual in the 1990s. He received a BA Degree from Nanjing University in 1986. He received an MA Degree from the Central Academy of Fine Arts in Beijing in 1989 and received a second MA Degree from Kassel Academy of Fine Arts in Germany in 1999. He also teaches Art Photography and Digital Media at the Central Academy of Fine Arts in Beijing. He was born in Wuxi, Jiangsu Province, and now lives and works in Beijing.

5Yan Xiaojing (b. 1978) is a contemporary mixed-media artist based in Shanghai, Nanjing, and Toronto. She is known for her sculpture, installation, and public artworks. Her current artwork focus on how nature transcends culture through references to the eternal and natural geological time. This research and exploration began with an artwork titled "Spirit Cloud," which is a large oscillating cloud composed of individual freshwater pearls suspended on filaments of fishing wire. She received a BA Degree in Decorative Arts from Nanjing University of the Arts. She also received an MA Degree from George Brown College in Toronto in Jewelry Design and an MA Degree from Indianna University of Pennsylvania in Sculpture. She is originally from Nanjing but currently works in Shanghai, Nanjing, and Toronto.

6Blair (b. 1996) is a member of staff in the Public Education Department at West Bund Museum in Shanghai, which is a privately funded contemporary art museum. She was born in Fujian but now currently lives and works in Shanghai. She received a BA Degree in Musicology from Shanghai Conservatory of Music and then received an MA Degree in Arts and Cultural Management from Pratt Institute in New York.

7CHILLCHILL (b.1990) is a digital artist, internet artist, and 3D animator. He also works in the club scene to exhibit his artworks. His artworks have an digital aesthetic and feature a dystopian exploration of society today and in the future. One recurring theme in his work is the "share economy," which is represented in examples of Mobike and Alipay. He graduated from Sichuan Fine Art Institute after studying Oil Painting. Several key galleries and museums have exhibited his work, including Ota Fine Arts, Shanghai. He is originally from Beihai and currently lives in Shanghai.

8Zhou Chengzhou (Armida) (周承舟; b. 1982) is a film director and photographer. His work is concerned with problems relating to the mental and spiritual wellbeing of individuals. Zhou's work also revolves around the concepts of industry, urbanization, and marginalization. He was born in Changde, Hunan. He studied literature and language and Peking University.

9Maggie Menghan Chen (Maggie) (b. 1998, Beijing) is a sculptor and mixed-media artist. She lives and works in Beijing and London. She obtained her MA Degree in Fine Art at Chelsea College of Arts following her BA degree in Art History at New York University. Maggie Menghan Chen explores the growth and metamorphosis of life in her sculpture works. Chen creates surreal sculptures that are hybrids of real and non-real elements. A lot of her inspiration comes from flora and fauna. Chen also sees her work as metaphors for the human psyche. The ferocity of beasts and delicacy of flowers represent the struggle between aggression and sensitivity in the mind.

10Ting Sun (Santy) is an Associate Professor in the School of Art at Nantong University. She is also a researcher at the International Public Art Association and Shanghai Collaborative Center for Public Art. She graduated from Shanghai Academy of Fine Arts with a doctorate degree. Her research interests include urban public art and rural revitalization. Currently she studies at as a visiting scholar in the department of Computer Graphics Technology at Purdue University. Her research is committed to constructing a Chinese path of public art evaluation from the perspective of public participation, taking into account the development characteristics of public art and the transformation needs of urban-rural integration. Her public art work "Yuyangli Square" was selected in the "2021-2022 China Landscape Design Yearbook," and she has acted as a curator of the 4th China Design Exhibition and Public Art Exhibition in 2023, and moderator of the 5th International Public Art Forum in 2020.

11Yan Zhou (b. 1988) is a new media artist. He is originally from Beijing, China. He received his BFA from the China Central Academy of Fine Art in 2012 and his Post-Baccalaureate Certificate in Visual Communication from the School of Art Institute of Chicago in 2013. He is currently MFA candidate 2015 in Visual Communication. Yan Zhou is an interdisciplinary artist and designer. His works combine daily experiences with conceptual understanding. Yan works with various media including printmaking, painting, drawing, films, and interactive design.

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Keywords: visual artists, digital artists, the creative industries, the contemporary art sector, digital work, AI, China

Citation: Duester E (2024) Digital art _work_ and AI: a new paradigm for work in the contemporary art sector in China. _Eur. J. Cult. Manag. Polic._ 14:12470. doi: 10.3389/ejcmp.2024.12470

Received: 25 November 2023; Accepted: 18 January 2024;  
Published: 21 February 2024.

Copyright (C) 2024 Duester. This is an open-access article distributed under the terms of the [Creative Commons Attribution License (CC BY).](http://creativecommons.org/licenses/by/4.0/) The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Emma Duester, ZW1tYWR1ZXN0ZXJAc2p0dS5lZHUuY24=

Disclaimer:  All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher. 

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Table of contents

  * Abstract
  * Introduction
  * AI adoption and regulation in China
  * Impacts of AI on art, the art industry, and work
  * Digital work as empowering
  * Digital artistic creation processes
  * Artists' creative (mis)uses of AI and digital software
  * Discussion
  * Conclusion
  * Data availability statement
  * Ethics statement
  * Author contributions
  * Conflict of interest
  * Acknowledgments
  * Supplementary material
  * Footnotes
  * References



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[Home](https://nhsjs.com/) __[NHSJS Reports](https://nhsjs.com/category/nhsjs-reports/ "View all posts in NHSJS Reports") __ The Future of AI Art and its Potential Interactions with the Art...

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# The Future of AI Art and its Potential Interactions with the Art Industry

By

[Andrew Hou](https://nhsjs.com/author/andrewyabohougmail-com/)

\- 

November 11, 2023

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## Abstract

Artificial Intelligence mimics human learning through algorithms and data analysis, with applications to visual art. Recent advances in AI art generators have raised concerns that these models may replace human artists one day. This paper considers various evidence on artistic quality, public reception, and economic impact to investigate this possibility. I reject the extreme position that AI art will completely replace human artists by highlighting the preference for human art both from important gatekeepers and the public. However, the opposite extreme is equally unlikely due to existing uses and investment in AI art. I conclude that AI and human artists can coexist and even complement one another.

## Introduction

In recent times, artificial intelligence (AI) has experienced explosive growth, with the researchers calculating the compound annual growth rate of the AI market to be 37.7% from 2023 – 20301. Generative AI, or AI algorithms that can produce unique digital content, such as texts and images, have prompted significant debates over its usage in the field of art.  As AI becomes more advanced, artists, art-buyers, and policymakers it has the potential to alter the landscape of the art industry. Many discussions have speculated on the long-term impacts of AI technology: some have brought up the concern that AI art can cause severe damage to the traditional art market as they are produced way cheaper and more efficiently2, while others proposed the idea of human artists collaborating with AI, a partnership that could enhance creativity3. However, the scarcity of empirical data and the rapidly changing AI technology have created a research gap for a more evidence-based and up-to-date investigation. The question this paper attempts to answer is how will the introduction of AI art impact the existing art industry and what will be the roles of human artists in this new age. Aside from the selective review of various public perspectives and investor reports, this paper also incorporates few unique sources such as Google Trends and the Bureau of Labor Statistics.  My research suggests that rather than homogenizing the art creation process, introducing AI can instead aid the industry’s continuous growth. 

## Background

AI is a multidisciplinary field  aiming to create systems that can  imitate human’s ability to learn and carry out tasks, such as making decisions and solving complex problems Within the field, machine learning algorithms analyze and learn from inputted data. It improves through training on large datasets and adjusting its internal parameters. This learning enables it to perform tasks, make predictions, and automate processes, mimicking human-like intelligence. Computer scientists are responsible for fine-tuning the models and guiding their development. Still, over time, AI also refines its own performance through continuous feedback and iterative improvement, enhancing its ability to handle complex tasks and make informed choices. AI can accomplish difficult tasks because programmers train the AI models on collected data related to specific functions to recognize patterns and increase their effectiveness at doing them. 

Scientists can also apply this same concept to the production of art. Since AI was in its early developmental stages, scientists have been experimenting with its ability to generate visual graphics and images. Max Bense, a German philosopher and mathematician, was partly responsible for applying computer algorithms to art. In the 1950s, he developed the concept of “information aesthetics,” which delves into how information, such as mathematical principles and patterns, can be manipulated to create artistic compositions. Another key figure in the development of this field was Harold Cohen, who created the famous program “AARON,” an AI system that can generate images using algorithms4. As time has passed and technological advances continue to accelerate, so do AI art generators. Some of the widely available and most popular AI art generators, including Stable Diffusion, Midjourney, and DALL-E-2, were all released in 2022. These models all use text prompts to generate intended art pieces. Users enter textual descriptions or keywords into the generators, which will associate the prompts with pre-existing images in its database and morph specific datasets into the final product. These recent models are not without their limitations. In the earlier stages of development, many art generators suffered from obvious errors in the produced artwork, notably their inaccurate depictions of human hands5. However, most believe these blatant failures are only temporary, and ensuing updates of art generators may resolve many of them without the need for radically new technology. For example, a software update in March of 2023 has significantly improved Midjourney’s ability to generate human hands6. Other limitations of art generators, such as general public’s concern for their potential plagiarisms or their supposedly lack of emotional values, are addressed in the competing perspective section.

### Current Situation

In recent times, especially since 2022, art created using artificial intelligence software has seen immense popularity. Social media platforms are flooded with discussions and posts regarding AI-generated images. Social media activity is a good indicator of widespread interest. For example, #aiart has over 8.5 million posts on Instagram as of July 2023. It is important to note that while this is not a tiny number, #drawing (287M posts) and #painting (165M posts) still outnumber #aiart by a large margin, partly due to the recentness of AI art’s popularity. Although this may not translate into important shifts in the market behavior of art consumers, it has been enough to concern many in the art world. This paper will discuss trends in interest in AI more in the competing perspective section. 

While many of the posts on social media were made by hobbyists, AI art is already used in prominent commercial applications. Netflix released an animated short film in early February 2023 titled “The Dog and the Boy,” incorporating AI-generated background art. Regardless of the film's low popularity due to issues regarding artists' payments, it has created some fears of a revolution in the art industry, in which AI replaces existing human artists. In the most recent strike by Hollywood screenwriters, workers sought protection against job loss from generative . This indicates that the film industry’s traditional gatekeepers have already acted against incursion from generative AI technology. This topic is discussed more in parts b and c of the competing perspective sections.AI art has also generated a lot of controversy concerning intellectual property rights. Many advocates for artists’ rights are upset that most AI models are trained on data and art without the artists’ consent or remuneration.. Kim Leutwyler, a famous painter, has expressed dismay over what they claim is AI’s immoral usage of their art: "It was very upsetting to see so many great Australian artists and emerging artists having their work used without their consent and then replicated in some form or another"7. They characterize AI as stealing artist’s creative capital, while also supplanting their labor. Legal actions that are coordinated by potential gatekeepers will be important to consider in predictions about the future of AI in art.

## Competing Perspectives on the Future of AI Art

Painting a complete picture of the future of AI art can be challenging. It depends on many unforeseeable changes in the future, such as the public perception of AI art or the invention of a radically more effective technology. Instead of pretending at certainty, we propose that the future of the art industry could lead down three different paths: AI could be just a disappointment in the field of art and become a plaything; AI could dominate the industry and ultimately drive out human artists and creators; lastly, AI could provide substantial benefits to current artists but will not be powerful enough to cause total replacement. We can analyze each scenario using prior research and relevant statistics to determine their plausibility.

### a. AI Art will never approximate human artist

This first section will analyze one of three scenarios: AI art will be unsuccessful in its implementation, and we will not see widespread usage within the art industry. This might happen because technology never elicits as positive a response as humans, or producers and consumers of art reject AI as intrinsically worse.  
There is some evidence that backs up this outcome. Researchers used thirty AI-generated images that they considered high quality and labeled half as “human-created” and the other half as “AI-created.” The study’s participants were asked to rate them across different criteria, such as beauty and profundity. Still, the results were staggering: the participants rated the “human-created” artworks higher across all requirements8. Even though AI created all images, simply labeling the artwork as created by AI will reduce their ratings. In a similar study conducted using AI music quality as the independent variable, participants perceived music created by AI to be of lower quality than regular music, highlighting the cognitive bias against AI art9. Companies need to be wary about using AI art if this bias is reflected across the general public. They will need to analyze the pros and cons: does the benefit gained from cutting costs using AI outweigh the loss of consumers due to their negative viewpoint towards AI products? If the answer is no, AI art might not be particularly successful in the art industry.  
Conversely, if this bias is because AI is new and unfamiliar, then this obstacle may soon be overcome. There are some evidence that suggests that the bias against AI art might not be inherent. In a study with 2000 adults weighted to be representative of the US adult population as a whole, only 27% of them viewed AI artwork. However, 56% of viewers enjoyed AI art, while only 19% did not. When asked for their preferences for AI art compared to human art, 34% actually stated they liked AI artwork more, while 29% valued human art more. The remaining 31% found them to be roughly equal10. In addition, if art is something created to evoke emotions by stimulating the senses, then even current AI technology meets that definition. Study participants asked to describe abstract AI art found pieces to be meaningful and perceived some intended emotions that are either positive or negative11. The study’s findings support artistic theories that state intended emotions can be interpreted by the human viewers with or without an artist, and abstract computer-generated art pieces also could evoke emotions. This shows that, even with the inherent bias towards artworks created by machines, the emotional value of the images is not zero. Suppose we account for all the additional improvements that will be added to these generative AI models in the future. In that case, they will surely be able to evoke more significant emotions inside the viewers. As such, the argument that AI cannot compete against human artists due to their inability to evoke emotions is not entirely valid.  
We now understand that AI art could evoke emotions of the same magnitude as human art. However, it still can be underwhelming: what if not enough workers within the art industry want to use it? Hayao Miyazaki from Studio Ghibli, considered one of the greatest animation directors who ever lived, did not hold back his criticisms towards a team of presenters after they showed him an AI-generated animation demo in 2016. In the NHK documentary titled “10 Years with Hayao Miyazaki”, he stated that “I am utterly disgusted. If you want to make creepy stuff, you can go ahead and do it.” He also goes on to say that AI art is “an insult to life itself” and that the creation of AI art means “we humans are losing faith in ourselves”12. British Author and Illustrator Rob Biddulph is also dismissive, saying AI art “is the exact opposite of what I believe art to be, … And simply pressing a button to generate an image is not a creative process”13. Many other artists share a similar sentiment. For example, on the popular platform Art Station, many artists have banded together and started posting the same image consisting of a prohibition sign over the word “AI" and a caption that says “NO TO AI GENERATED IMAGES.” Not only is AI art a potential replacement for current artists, but the method by which AI creates art is questionable. If most artists continue to either give AI art the cold shoulder or are actively fighting against it, AI art might not be widely used within the industry and ultimately die out.  
To counter the previous point, many workers within the art and design industry are already exploring different ways to incorporate generative AI into their fields. According to a New York Times article, many digital artists are already checking the new technologies out for themselves14. Whether an interior designer is using AI to create a mock-up for a client or a filmmaker making early concept art, AI is a helpful tool that can boost productivity through novel approaches and reduce the number of time-consuming tasks artists must deal with. If artists cooperating with AI art models start to gain a competitive advantage against others within the same field, refusing to resort to the models due to the increased efficiency, then the remaining artists may be forced to use AI due to company expectations. More discussion related to implementation of AI technologies can be found in part c.  
One last indication that AI art will not outright fail is the massive investment that developers of AI art generators are receiving: If AI art generators receive many investments from venture capitalists, it is a sign that these investors, at least, expect it to be valuable in the future. Stability AI, the leading open-source generative AI company and the creator of the renowned Stable Diffusion, received $101 million in funding that values the company at around $1 billion back in October of 202215. Coatue, Lightspeed Venture Partners, and O’Shaughnessy led the funding. OpenAI, the creator of the famous Dall-E 2 and ChatGPT received an even greater investment. Microsoft put around $10 billion into the company this January, a monumental deal that gave OpenAI limitless potential. They also received $300 million from VC (venture capitalists) firms, including Sequoia Capital, Andreessen Horowitz, Thrive, and K2 Global. The company was valued at an incredible $27 - $29 billion when the funding had occurred, and it may very well still be growing today16. All of these investments mean that, according to venture capitalists, AI art generators have a very high potential to be successful in the industry.  
Although based on the current situation, AI art is most likely going to be significant in the future of the art industry, several factors may make AI’s future impacts minimal. Pending legal actions against AI art generators for issues such as copyright infringement could potentially limit AI's role within the artistic community. Additionally, intellectual movements that intensifies public’s apprehension regarding AI technology. Much like the depictions within Samuel Butler's article “Darwin Among the Machines”17 in which Butler raised the possibility that machines may overtake humans as the dominant species, many current policy makers may reflect the same perspective and purposefully hinders the implementation of AI art in order to prevent human artists from being taken over. There’s already existing support for limiting the fast-paced technological development, and such viewpoints may become even more popular, leading to a consumer demand favoring purely human art. These evolving legal and ideological shifts could act as barriers to the legitimacy of AI-generated art, making them less likely to be realized.

### b. AI art will replace human-generated art

This section discusses the possibility that are feared by many artists: AI art will one day  
overtake human art as the most prevalent art form. According to a New York Times article, AI art is already winning art competitions. In 2022, an entrant to the Colorado State Fair’s annual art competition won a prize by submitting his work, which was created through Midjourney18. This news may seem somewhat irrelevant, but it marks one of the first instances where human creation lost against AI. As AI art continues to absorb data of art readily available on the web, it grows and improves to the point where it is more than capable of competing against human art.  
AI has now arrived at the advanced stage where it is able to be widely implemented in the workforce, and data shows that companies are learning effective ways to use AI to boost workers’ work rate. The McKinsey Global Survey offers a general outlook of AI as a whole. While these data do not go into details regarding AI generative art’s usage rate, the information does show that companies are more open to investing and incorporating AI into their companies. The results indicated that companies are slowly but surely finding ways to use different artificial intelligence capabilities in their line of business, with the average number of used AI capabilities reported being 3.8 in 202219. That is not all. The survey notion that AI leaders, or companies that excel at adapting to AI usage, are rapidly pulling ahead of their competitors. As such, more and more companies are joining the AI movement. In 2018, 40% of the respondents to the survey said their companies “reported more than 5% of their digital budgets went to AI,” and now, in 2022, 52% of respondents “reported that level of investment.” 63% of companies also replied that they would be increasing their investment in AI within the next three years. This may reflect the perspective of companies specializing in graphic design and other visual art-related industries, who may utilize generative AI to a greater extent. If this trend continues and more companies rely on AI, human artists may be forced to use AI technology to keep up with competitors.  
While it is possible for artistic companies or free-lance artists to adopt the usage of AI into their work, the exact effects this adoption will have on the numbers of human artists is still murky. The U.S. Bureau of Labor Statistics’ Occupational Outlook Handbook predicts no significant changes within the current employment rates of the various occupations within the Arts and Design industry. Within it, the job outlook of art directors (4%), crafts and fine artists (6%) are growing at a standard pace, while the job outlook of Fashion Designers (3%), Graphic Designers (3%), and interior designers (3%) is growing slower than usual, but still growing all the same20. According to these projections, the industry is not facing significant changes related to the number of workers in the upcoming years. However, it is important to note that these results are made with data collected from 2021, when most AI art generators are not officially published. The projection may change by adding this factor into the equation.  
AI art also may not be as popular as people are making it out to be. The influx of AI art into social media feeds often slowly diminishes with time. We used Google Trends to analyze the search volume and relative interest in specific topics over time. While it may not provide comprehensive data on the concrete usage data of AI applications, it can offer a directional understanding of public interest and trends. Google Trends displays a graph that shows the interest in a search term over time, and the y-axis represents the interest relative to the highest point in that time interval, which is chosen to be the last 12 months. “AI art” was first inputted, and Trends displayed that the term peaked during the week of December 4th, 2022. Before that, the average relative interest to the peak from June to December was around 20%, and after that, the average relative interest rate slowly dwindled back to 20% till this day21. After the initial boom, people showed less interest due to the lack of significant updates. While newer versions for AI models such as Midjourney are being released periodically and are enhancing their performance to a certain degree, a more sweeping update is required to draw back the attention of the public. Additionally, I compared “AI art” to regular art topics such as “drawing” and “painting,” all of which received significantly more interest. Currently, the interest in “AI art” is roughly equal to “watercolor” within the past 90 days (from July 3, 2023). When we entered generative AI models by name, “Midjourney” was at a 40% interest rate, DALL-E was 18%, and Stable Diffusion was 75%. None of these models are near their most popular state, and this decrease in renown could very well hinder their ability to compete with traditional art forms with a stable popularity. With the current trends, another innovative generative feature is needed to bring AI art back into the spotlight. Overall, while AI art can be a threat to the art industry with additional capabilities, it is not likely that the change will occur with its current rate of progression.  
One last obstacle that may stop the proliferation of AI art is its legal issues. Although the Hollywood strike was mainly concerned with the film industry, visual artists may soon follow suit and commence large scale operations resisting the increasing usage of AI in the visual art industry. Such movements may have already gained traction: some artists have filed lawsuits regarding AI’s incorporation of online art into its pile of data. Three artists sued Stability AI, Midjourney, and DeviantArt, the developer of DreamUp, and claimed that these companies’ AI generators are trained on their copyrighted artworks without first obtaining their consent22. In a separate lawsuit, the stock image company named Getty Images is suing Stability AI for stealing their artwork and copying around 12 million of their copyrighted artwork23. There is little precedent that the Courts can look back on, as AI is a brand-new technology. Depending on the results of these cases that determine whether AI models can train off copyrighted materials, the freedom of AI art may be significantly restricted. Artists and other workers within the creative industry are also actively taking measures against the invasion of AI that may replace them: many artists are choosing to “opt out” of allowing AI developers to incorporate their artworks into the training data for the AI models24. Many websites and apps gatekeep their content so AI does not consume them. As such, not only will the models be unable to access data sources to train on, there will most likely be exclusive artworks that are illegal to recreate using an AI. This means that while AI artists may still become very powerful, human artists will have a few ways to capitalize within the industry.  
Some dramatic alterations to the current situation could tip the scales in favor of AI over human artists, such as the extraordinary technological advancements in AI art generation. As the current leading AI art generators continue to receive updates, the advancements in neural network architectures and data processing capabilities might enable AI to generate art that are unprecedently complex and emotional. Coupled with this, shifts in consumer tastes may also contribute to this scenario. As the younger, more tech-savvy generations grow up exposed to AI-generated art, their preferences could evolve to favor the novel aesthetics that AI offers. This blend of advanced technology and changing consumer attitudes could create an environment where AI-generated art is able to fully compete or even outperforms human art, potentially becoming the primary artistic medium.

### c. AI will be a useful complement to human art

This option is the most likely to be realized. The two previous paths are both on the extreme ends, and we went over why they might not be sensible. If AI is not a failure but will not wholly overtake the industry, its degree of helpfulness lies in the middle. In a research article that interviews scientists and artists to determine AI's role in the creative process, interviewees recognized that AI could be a great aid in various steps of the creative process. Instead of AI being a fully functional replacement, the artists can utilize AI’s ability to produce “surprising and interesting results” and use that as inspiration for their work25. As such, some artists do not outright reject the future of co-creation with the help of AI and instead are open to exploring the possibilities that come with the nifty new tools. The Writers Guild of the Hollywood strike shares this sentiment. In their negotiation with the Alliance of Motion Picture, they agreed that AI can be a very useful tool if used in with ethical considerations in mind. The final deal agreed to protect the writers’ contracts involving AI, allowing them to use AI with permission from the company. However, they cannot be forced by their company to use AI in their work26.  
The general public, while not experts by any means, expects AI technologies in the art field to be just a minor advancement, and not a revolutionizing invention. This viewpoint may be helpful because it is largely unbiased: it doesn’t belong to artists who feel their careers are at risk, and also doesn’t belong to businessmen who may benefit from the promotion of AI art models as their valuations are on the rise. A research survey held by Pew Research Center tries to grasp how big of an impact Americans believe AI will have in different applications. 31% of respondents believe AI image production is a significant advancement, 39% say it is a minor advancement, 19% say it is not an advancement at all, and the remaining 11% are not sure27. The same respondents instead think the development of AI in other fields, such as biology, agriculture, and weather prediction, is more valuable. For example, 59% of respondents view AI’s ability to predict protein structures within cells to be a significant advancement, 27% say it is a minor advancement, and only 4% think it is not an advancement. Essentially, the educated public believes that AI in the art industry is an advancement but its impacts are limited, and its effects won’t be as dramatic as some of the AI applications in other fields. This comparison shows that the public believes AI art is not poised to replace human workforce on the same scale as AI is impacting other sectors, and being merely a minor advancement allows for the existing method of creating art to still be relevant, even with the increasing usage of art generators. It seems that to them, collaboration between humans and AI is more plausible than AI outright replacing a section of the workforce.  
The leading investment company Goldman Sachs performed a research study to break down the potential effects of artificial intelligence on the current workforce. Their analysis estimated how much specific industries in the US will be impacted by AI and automation. They predicted that one-fourth of the total current workforce is exposed to the threat of automation, and the Arts, Design, Entertainment, Sports, and Media (ADESM) Industry’s exposure is only slightly above average at 26%28. This means that 26% of the industry is at risk of being replaced by AI, but it doesn’t necessarily mean they will for sure be replaced. If this same percentage applies to the art industry specifically as well, then most of the workers with art-related jobs are safe, as only a maximum of 26% of them could lose their jobs. The paper also indicates that less than 5% of the ADESM industry are jobs with above 50% of complex tasks that are exposed to AI automation. Around 20% of the industry is entirely unaffected, and the rest of the 75% will continue to work, only now will they be complemented by AI. Compared to the other industries, ADESM careers are less likely to be wholly replaced and more likely to be complemented by AI. It seems that generative AI will become a stable part of the art industry while not completely eradicating existing careers.  
How exactly is this scenario of the visual art industry, now intermingled with AI technology, better in any way from the pre-AI era? There aren’t enough data from available research that studies AI’s large-scale effects the industry, but this paragraph proposes a few hypothetical effects through logic and reasoning. Firstly, as discussed above, AI can aid artists individually in various steps during their creative process. By utilizing AI’s fast generation speed, artists can likely rapidly journey through the trial-and-error stage by creating various drafts of their early ideas. This will give them a clear idea on what direction they want to take their art. Moreover, unexpected features within AI art pieces can serve as inspiration to artists to implement into their own work, allowing them to add layers that wouldn’t have existed before. This effect on the art-creation process will likely be very significant, as the previous paragraph suggests that an extremely low percentage of jobs in the ADESM industry are actually in danger of replacement by automation, meaning that most human artists that are capable of altering their creative process using AI and not be completely replaced are the majority.  
Another potential effect is on the economy of the visual art industry, benefited from the concept of normal goods and inferior goods. Normal goods are products that consumers buy more of as their income rises, and inferior goods are the opposite: they are things that people stop buying once they are more well-off, such as junk food or used shoes. As such, normal goods are often associated more with a luxurious lifestyle, and inferior goods are things people buy to save costs. There aren’t many studies on the average number of art pieces per households based on income, therefore it’s up to debate whether or not they should be considered as non-essential normal goods. However, it’s reasonable to assume that wealthier people tend to have more art pieces due to various reasons: bigger estate provides them more rooms for art display, and higher income allows for the hobby of art collection. Following that assumption, AI can incentivize an increase in the purchasing rate of art pieces in two ways. Firstly, more efficient production of art pieces allows for a cheaper final price, allowing lower-income buyers to purchase more art. Secondly, research has predicted that implementation of AI technology increases productivity so much that it could add between $13.6 trillion to $22.1 trillion annually to the global economy29. If this benefit is applicable to the U.S. economy as well and our average standard of living increase, then this may allow the general population to purchase even more art as they move on from buying inferior goods to buying normal goods. Both of these options will incentivize the purchasing of more art pieces, leading to a more vibrant visual art economy.  
Following up on the topic of economy, there is the concept of normal goods and inferior goods. Normal goods are products that consumers buy more of as their income rises, and inferior goods are the opposite: they are things that people stop buying once they are more well-off, such as junk food or used shoes. As such, normal goods are often associated more with a luxurious lifestyle, and inferior goods are things people buy to save costs. Following the phenomenon above, it makes sense why the introduction of digital art was not detrimental to the art industry: all it did was make the art and design process easier, which in turn allowed for cheaper production of art and graphic designs, leading to an increased rate of consumption from buyers. If we applied that same reasoning, wouldn't AI art, which is much cheaper to make, incentivize people to purchase more art due to the price cut? Some may argue that AI-produced art's inferior quality may negate this effect. However, it is reasonable to believe that once generative has advanced to a certain point, the art quality will no longer be a concern; all that matters is the composition and art style.

## Conclusion

The art industry is currently approaching a potentially critical turning point. The introduction of AI technology may very well revolutionize the way it currently operates, and this paper serves to allow companies and artists to better prepare themselves for the new age. We predict that the future of the art industry will not lean towards either side of the extremes of X or Y but instead take the moderate paths. The new industrial ecosystem will likely consist of AI and humans learning to co-exist, with human and AI artists finding their respective audiences. For now, many career paths in the art industry should still be viable; only slight alterations will occur as artists begin to explore new realms with the assistance of generative AI. Furthermore, regular people will continue making art without being influenced by the changes in the art industry. After all, many artists enjoy the act of creating their pieces, and art creation will be preserved as a fun hobby for many years to come. 

This study has several limitations that should be noted. Firstly, the scope of our research does not encompass all forms of art; theater and sound arts are notably excluded. Moreover, various subfields of visual arts like ceramics, architecture, and films and animation are not yet generated by AI, limiting our ability to make accurate predictions about these areas. Additionally, while the paper aims to provide a comprehensive overview, it does not claim to cover the art industry in its entirety, nor does it incorporate all available evidence. The predictions made are intentionally broad and lack specificity, reflecting the general nature of the paper. For more specialized discussions, such as legal aspects surrounding AI-generated art, further research is required in order to gain insight about these topics, which will ultimately contribute to mapping out the nuance of the art industry’s future.

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url: http://arxiv.org/abs/2307.07521v1
title: [2307.07521v1] Artistic Strategies to Guide Neural Networks
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# Computer Science > Human-Computer Interaction

**arXiv:2307.07521v1** (cs) 

[Submitted on 6 Jul 2023]

# Title:Artistic Strategies to Guide Neural Networks

Authors:[Varvara Guljajeva](https://arxiv.org/search/cs?searchtype=author&query=Guljajeva,+V), [Mar Canet Sola](https://arxiv.org/search/cs?searchtype=author&query=Sola,+M+C), [Isaac Joseph Clarke](https://arxiv.org/search/cs?searchtype=author&query=Clarke,+I+J)

View a PDF of the paper titled Artistic Strategies to Guide Neural Networks, by Varvara Guljajeva and 2 other authors

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> Abstract:Artificial Intelligence is present in the generation and distribution of culture. How do artists exploit neural networks? What impact do these algorithms have on artistic practice? Through a practice-based research methodology, this paper explores the potentials and limits of current AI technology, more precisely deep neural networks, in the context of image, text, form and translation of semiotic spaces. In a relatively short time, the generation of high-resolution images and 3D objects has been achieved. There are models, like CLIP and text2mesh, that do not need the same kind of media input as the output; we call them translation models. Such a twist contributes toward creativity arousal, which manifests itself in art practice and feeds back to the developers' pipeline. Yet again, we see how artworks act as catalysts for technology development. Those creative scenarios and processes are enabled not solely by AI models, but by the hard work behind implementing these new technologies. AI does not create a 'push-a-button' masterpiece but requires a deep understanding of the technology behind it, and a creative and critical mindset. Thus, AI opens new avenues for inspiration and offers novel tool sets, and yet again the question of authorship is asked. 

Subjects: |  Human-Computer Interaction (cs.HC); Computers and Society (cs.CY)  
---|---  
ACM classes: | I.2.0; I.2.m  
Cite as: | [arXiv:2307.07521](https://arxiv.org/abs/2307.07521) [cs.HC]  
  | (or  [arXiv:2307.07521v1](https://arxiv.org/abs/2307.07521v1) [cs.HC] for this version)   
  |  <https://doi.org/10.48550/arXiv.2307.07521> Focus to learn more arXiv-issued DOI via DataCite  
  
## Submission history

From: Mar Canet Sola [[view email](/show-email/e4f276c7/2307.07521)]   
**[v1]** Thu, 6 Jul 2023 22:57:10 UTC (5,190 KB)  


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url: https://arxiv.org/html/2502.03940v1
title: Generative AI and Creative Work: Narratives, Values, and Impacts
scrape_date: 2025-03-11T13:24:50.596369
content_type: markdown
hash: 8955f8fc85295c193a14e43fadcb185770b4eda2f2492a38e234675c04775d2f
content:
  1. [1 Introduction](https://arxiv.org/html/2502.03940v1#S1 "In Generative AI and Creative Work: Narratives, Values, and Impacts")
  2. [2 Background](https://arxiv.org/html/2502.03940v1#S2 "In Generative AI and Creative Work: Narratives, Values, and Impacts")
    1. [2.1 AI Narratives](https://arxiv.org/html/2502.03940v1#S2.SS1 "In 2. Background ‣ Generative AI and Creative Work: Narratives, Values, and Impacts")
    2. [2.2 AI in the Arts](https://arxiv.org/html/2502.03940v1#S2.SS2 "In 2. Background ‣ Generative AI and Creative Work: Narratives, Values, and Impacts")
  3. [3 Method](https://arxiv.org/html/2502.03940v1#S3 "In Generative AI and Creative Work: Narratives, Values, and Impacts")
    1. [3.1 Corpus creation](https://arxiv.org/html/2502.03940v1#S3.SS1 "In 3. Method ‣ Generative AI and Creative Work: Narratives, Values, and Impacts")
    2. [3.2 Corpus filtering and analysis](https://arxiv.org/html/2502.03940v1#S3.SS2 "In 3. Method ‣ Generative AI and Creative Work: Narratives, Values, and Impacts")
  4. [4 Findings](https://arxiv.org/html/2502.03940v1#S4 "In Generative AI and Creative Work: Narratives, Values, and Impacts")
    1. [4.1 Automation over manual work](https://arxiv.org/html/2502.03940v1#S4.SS1 "In 4. Findings ‣ Generative AI and Creative Work: Narratives, Values, and Impacts")
    2. [4.2 Efficiency over exploration](https://arxiv.org/html/2502.03940v1#S4.SS2 "In 4. Findings ‣ Generative AI and Creative Work: Narratives, Values, and Impacts")
    3. [4.3 Concept over execution](https://arxiv.org/html/2502.03940v1#S4.SS3 "In 4. Findings ‣ Generative AI and Creative Work: Narratives, Values, and Impacts")
    4. [4.4 Artefact over process](https://arxiv.org/html/2502.03940v1#S4.SS4 "In 4. Findings ‣ Generative AI and Creative Work: Narratives, Values, and Impacts")
    5. [4.5 Short-term over long-term skills](https://arxiv.org/html/2502.03940v1#S4.SS5 "In 4. Findings ‣ Generative AI and Creative Work: Narratives, Values, and Impacts")
  5. [5 Discussion](https://arxiv.org/html/2502.03940v1#S5 "In Generative AI and Creative Work: Narratives, Values, and Impacts")



# Generative AI and Creative Work: Narratives, Values, and Impacts

Baptiste Caramiaux  [caramiaux@isir.upmc.fr](mailto:caramiaux@isir.upmc.fr) CNRS, Sorbonne Université, ISIRParisFrance ,  Kate Crawford  Microsoft ResearchNew York CityUSA ,  Q. Vera Liao  Microsoft ResearchMontrealCanada ,  Gonzalo Ramos  Microsoft ResearchRedmondUSA and  Jenny Williams  MicrosoftUSA

###### Abstract.

Generative AI has gained a significant foothold in the creative and artistic sectors. In this context, the concept of creative work is influenced by discourses originating from technological stakeholders and mainstream media. The framing of narratives surrounding creativity and artistic production not only reflects a particular vision of culture but also actively contributes to shaping it. In this article, we review online media outlets and analyze the dominant narratives around AI’s impact on creative work that they convey. We found that the discourse promotes creativity freed from its material realisation through human labor. The separation of the idea from its material conditions is achieved by automation, which is the driving force behind productive efficiency assessed as the reduction of time taken to produce. And the withdrawal of the skills typically required in the execution of the creative process is seen as a means for democratising creativity. This discourse tends to correspond to the dominant techno-positivist vision and to assert power over the creative economy and culture.

Genrative AI, Creativity, AI Narratives, Art 

##  1\. Introduction

The spread of generative artificial intelligence (generative AI) in the creative and artistic sectors is generating intense debate among academics, journalists, artists, policy makers and the general public. These conversations raise fundamental questions about art and human creativity, tackling subjects of cultural and philosophical interest, as well as questions about the future of the creative and artistic sectors (Caramiaux et al., [2019](https://arxiv.org/html/2502.03940v1#bib.bib10); European Commission et al., [2022](https://arxiv.org/html/2502.03940v1#bib.bib24); World Economic Forum, [2018](https://arxiv.org/html/2502.03940v1#bib.bib66)). In parallel, the economic incentives surrounding AI in the creative industries further shape both public discourse and perceptions of artistic creation. This article examines the overlooked aspects of AI narratives in the arts, with a particular focus on how advances in generative AI technology and its implication in creative practices contribute to narratives about creative work.

Narratives are cultural artifacts: stories that convey particular points of view or sets of values (Bal, [2009](https://arxiv.org/html/2502.03940v1#bib.bib4)). Narratives accompany, and sometimes drive, technological innovation as an element of communication for various audiences, including the general public, promoting cultural views and fostering its adoption (Turner, [2010](https://arxiv.org/html/2502.03940v1#bib.bib62)). The stories create a bridge between the complex world of science and technology and the world of cultural representation of these technologies. These narratives become an important vector of adoption or rejection for people. For example, fictions around AI fuel existential fears that are taken up by actors in the field (Cave and Dihal, [2019](https://arxiv.org/html/2502.03940v1#bib.bib12)), particularly in a number of companies and think tanks, and have a significant impact on innovation and policy making (Gebru and Torres, [2024](https://arxiv.org/html/2502.03940v1#bib.bib27)).

When narratives touch on creative work and culture, they shape the way the general public perceives AI as well as the way they perceive artistic creation and creativity in general. And more importantly, the narratives used to convey its use in this environment, such as accessibility for all and effortless creativity, can have major consequences for culture in our contemporary societies. In this article, we investigate what constitutes the dominant narratives that are conveyed about generative AI technology in the fields of creativity and artistic creation. Through the analysis of these narratives, we investigate the following questions: Since narratives are told by different parties, who is telling the story of AI’s benefits for artistic creation? What explicit values do these narratives convey? What implicit assumptions underlie these values, and how do they influence or hinder creative work?

In order to address these questions, we reviewed the narratives in online media publications that show up in relevant searches from popular search engines. The narratives are to be understood in the plural, as they arise from various intertwined perspectives. In these, however, we show that the voices are primarily not those of artists, but of journalists and tech actors. When artists have spoken on this topic, a different narrative emerges, one that is contrary to the dominant narrative from tech companies. We found that the discourse on creative work, when contextualised in AI, promotes the concept freed from its material realisation, such as craft skills, messy processes, markets. The separation of the idea from its material realisation is achieved by automation, which is the driving force behind productive efficiency. The idea takes precedence over the execution, which must be reduced to a minimum in terms of time. The withdrawal of the skills typically required in the execution of the creative process is then placed in the perspective of a democratization of creativity through the acquisition of rapid skills rather than long-term ones. This vision of creative work then creates tensions and a hierarchy in the economy of the creative sectors.

This work contributes to research into the impact of generative AI on the creative and cultural sectors, in line with work alerting to the problems associated with injunction in the use of AI in art (Jiang et al., [2023](https://arxiv.org/html/2502.03940v1#bib.bib35); Donnarumma, [2023](https://arxiv.org/html/2502.03940v1#bib.bib21)). We emphasize here that the dominant vision is a partial one, which is primarily not narrated by artists and which, in fact, reinforces artists’ fears and anxieties about this technology, which will eventually prevent them from using it in an inspiring and original way (Jiang et al., [2023](https://arxiv.org/html/2502.03940v1#bib.bib35); Zapata, [2023](https://arxiv.org/html/2502.03940v1#bib.bib70)).

##  2\. Background

###  2.1. AI Narratives

AI is not a neutral technology or a neutral term. There are narratives accompanying AI, where narratives are understood as cultural artifacts of various kinds that tell stories and convey particular points of view or sets of values [3]. AI narratives have been carried by many means: fictional cultural artifacts such as science fiction writings and films, marketing slogans or product value propositions, tech articles in the media, interviews with product leaders, or cultural critiques.

AI narratives have to be taken seriously because they can heavily influence public acceptance of the technology and they can also shape the way policymakers see the technology. The power of using the cultural dimension of AI was understood by tech companies, who, with a view to communicating with customers, rebranded machine learning (a discipline and set of techniques) as AI after the breakthrough in the field in the 2010s (Whittaker, [2021](https://arxiv.org/html/2502.03940v1#bib.bib63)). The discourse aimed to communicate technological products as having major significance in terms of scientific innovation. Paradoxically, these predictive technology products are opaque and difficult to interpret, which has led to their behavior being likened to a form of magic. Campolo and Crawford (Campolo and Crawford, [2020](https://arxiv.org/html/2502.03940v1#bib.bib8)) describe this phenomenon as “enchanted determinism,” whereby the perception of technology as magical and deterministic — thus outside of human control — allows companies to deflect accountability for the full impacts of their systems. In a recent article, Chubb et al. point out that dominant narratives are “polarized between notions of threat and myopic solutionism” (Chubb et al., [2024](https://arxiv.org/html/2502.03940v1#bib.bib16)). In their article, the authors propose to explore the missing stories, especially about the everyday impacts of technology. These polarized narratives are fueled by fictions that often depict simplified stories and exemplify the fears and hopes of AI technology [11], which eventually mirror simplified emotions and values of our society. As Herman puts it: “[…] the desire to tell dramatic stories requires certain types of AI, for example humanoid robots or almighty systems. Thus, second, science-fictional AI is not necessarily about the technology but can be a metaphor for other social issues.” (Hermann, [2023](https://arxiv.org/html/2502.03940v1#bib.bib31)) (p. 320).

AI narratives also carry stereotypes. These stereotypes may, for example, be linked to how race and ethnicity are represented. In a recent study titled “Whiteness of AI”, Cave and Dihal (Cave and Dihal, [2020](https://arxiv.org/html/2502.03940v1#bib.bib13)) argue that “AI is predominantly racialised as White because it is deemed to possess attributes that this frame [White racial frame] imputes to White people” (p. 696). They explain that there is a historical exclusion of non-men and non-whites from positions that are the best paid and considered professional, which is present in the way AI is represented (Cave and Dihal, [2020](https://arxiv.org/html/2502.03940v1#bib.bib13); Noble, [2018](https://arxiv.org/html/2502.03940v1#bib.bib50)). Other recent works have shown how race should be considered, especially the role of people of color in the development and use of technology and the persistent raciale biases in contemporary technology (Benjamin, [2020](https://arxiv.org/html/2502.03940v1#bib.bib5)). The very choice of terms used has a territorialist and colonialist connotation: the name given to machine learning models has evolved from pre-trained model, foundation model to frontier model (Gebru, [2024](https://arxiv.org/html/2502.03940v1#bib.bib26)). However, the paradox, as Alondra Nelson has shown, is that narratives tend to portray technology as erasing distinctions between race, gender or ability (the libertarian viewpoint), but end up promoting exclusive technology (Nelson, [2002](https://arxiv.org/html/2502.03940v1#bib.bib48)).

These studies show that technology is not neutral and that cultural studies of AI are important to shed light on its impacts. It is then surprising that few studies focus on this problem in the context of the creative and artistic sectors. These sectors are structuring the cultures of our societies. The narratives on technologies that have the capacity to transform these sectors then have the capacity to transform our cultures.

###  2.2. AI in the Arts

The use of AI in artistic practices has a long history. The Aaron software, created by artist Harold Cohen to produce drawings autonomously, is an example of the first experiments in integrating AI into artistic practice. Similarly, Vera Molnár notoriously used autonomous procedures to create repetitive patterns of deformable geometric shapes. In music, back in 1956, Lejaren Hiller first experimented with the first computers and implemented complex processes that have been automated to generate a musical score, called IIliac suite for String Quartet. Generative forms of music based on computer processes were then explored by numerous composers such as Daphne Oram with Oramics and Laurie Spiegel. In the 2000s, David Cope developed musical intelligence experiments. François Pachet, in France, developed musical algorithm based Markov chains as a composition and improvisation tool (Pachet, [2003](https://arxiv.org/html/2502.03940v1#bib.bib51)).

The use of AI in artistic practice spread wider when first data-driven learning technologies were used to create visual art. In 2012, the deep learning breakthrough (Krizhevsky et al., [2012](https://arxiv.org/html/2502.03940v1#bib.bib39)) showed that Machine Learning (ML), a set of statistical methods able to perform prediction and generation based on learned structure from a dataset, has the capacity to generate images, sounds or texts that are creatively, and artistically interesting (Akten, [2021](https://arxiv.org/html/2502.03940v1#bib.bib2)). While not specifically targeting artists and creatives, AI technology based on deep learning started to be appropriated by a small community of artists that experimented with newly released AI techniques (Caramiaux and Fdili Alaoui, [2022](https://arxiv.org/html/2502.03940v1#bib.bib11)). One of the first deep learning models used was the inception algorithm (or DeepDream), designed by Google engineers and artists (Mordvintsev et al., [2015](https://arxiv.org/html/2502.03940v1#bib.bib45)). AI Art then greatly proliferated after the publication of the Generative Adversarial Network (GAN) algorithm in 2014 (Goodfellow et al., [2014](https://arxiv.org/html/2502.03940v1#bib.bib28)) and its extensions (see for instance, (Brock et al., [2018](https://arxiv.org/html/2502.03940v1#bib.bib6); Radford et al., [2015](https://arxiv.org/html/2502.03940v1#bib.bib55))). This family of techniques made it possible to generate good-quality images, refine the model’s aesthetics by training it on a selection of curated images, and explore the abstract visual space learned by the model, creating surprises and interesting accidents for artists. These experiments started out as niches, often run by artists hacking code from Github. Then these experiments began to attract attention: an exhibition was organized at Google in 2015, and the Ars Electronica festival dedicated its annual theme to AI in 2017. In 2018, Christie’s auction house sold a digital artwork created using GAN. Communication started to be important: this artwork was publicized as the first work created by AI (Daniele and Song, [2019](https://arxiv.org/html/2502.03940v1#bib.bib19)) and was sold several hundred times higher than the estimated price when the house first put the artwork up for sale.

Media coverage of AI’s uses in the artistic and creative sectors expanded following more recent AI innovations. In 2020, innovations in the AI field demonstrated that larger ML models could mimic the structure of natural language, such as English, and generate plausible continuation of a provided sentence (Brown, [2020](https://arxiv.org/html/2502.03940v1#bib.bib7)). Similar model architectures were then used to learn relationships between text and image, allowing generating images based on inputs of textual descriptions (Radford et al., [2021](https://arxiv.org/html/2502.03940v1#bib.bib54)). Shortly after, companies released text-to-image systems, such as OpenAI’s DALL-E111<https://openai.com/index/dall-e-2/>, MidJourney222<https://www.midjourney.com>, and Stable Diffusion333<https://stability.ai/news/introducing-stable-diffusion-3-5>. These systems were explicitly marketed to creatives and artists. They offer image outputs with high levels of realism and polish, are easy to use and support versatile use cases, contributing to their popularity across diverse communities. By employing natural language as input, these systems provide a way to lower the technical barrier for content creation (Chang et al., [2023](https://arxiv.org/html/2502.03940v1#bib.bib14); Sanchez, [2023](https://arxiv.org/html/2502.03940v1#bib.bib57)). Their simplicity of interaction and versatility mean they can be used for production as well as ideation (Rajcic et al., [2024](https://arxiv.org/html/2502.03940v1#bib.bib56)). Prompting becomes part of this new artistic practice: prompts can be viewed as artworks in their own right, much like the model-generated outputs they produce, as well as tools by artists who frequently employ prompt templates to craft these outputs (Chang et al., [2023](https://arxiv.org/html/2502.03940v1#bib.bib14)). In 2022, the cover of the general public magazine Cosmopolitan was produced using an AI-powered tool for generating images from text input. The magazine communicated that “The World’s Smartest Artificial Intelligence Just Made Its First Magazine Cover”444<https://www.cosmopolitan.com/lifestyle/a40314356/dall-e-2-artificial-intelligence-cover/>, , and “it only took 20 seconds to make”. This exemplifies the rapid integration of AI into mainstream media and underscores the growing role of AI in creative and commercial domains. And this was accompanied by a backlash from the artistic community (see, for instance, Steven Zapata’s presentation (Zapata, [2022](https://arxiv.org/html/2502.03940v1#bib.bib69))). Firstly, it became public that these new models were trained on databases containing copyrighted content, owned by the artists (Xiang, [2022](https://arxiv.org/html/2502.03940v1#bib.bib67); Baio, [2022](https://arxiv.org/html/2502.03940v1#bib.bib3)). In addition, the assimilation of prompting practice with artistic practice was seen as a diminishing view of creativity (McCormack et al., [2023](https://arxiv.org/html/2502.03940v1#bib.bib42)).

AI artists are not only engaging with the technical aspects of AI, but also its cultural and political dimensions. Through their work, they are critiquing the culture of AI research, highlighting its focus on technical performance at the expense of social and ethical considerations (Caramiaux and Fdili Alaoui, [2022](https://arxiv.org/html/2502.03940v1#bib.bib11); Kawakami and Venkatagiri, [2024](https://arxiv.org/html/2502.03940v1#bib.bib36)). They are also using their art to raise awareness about issues of bias, labor exploitation, and power dynamics inherent in AI systems (Caramiaux and Fdili Alaoui, [2022](https://arxiv.org/html/2502.03940v1#bib.bib11); Hemment et al., [2023](https://arxiv.org/html/2502.03940v1#bib.bib30)). By making these issues visible, AI artists are contributing to a broader societal discussion about the responsible development and deployment of AI.

These latter works bring to the fore elements of discussion, on the part of artists, about what is involved in using AI in creative work. In this article, we question whether these elements can be found in the narratives around AI in art, when communicated to the general public through the media. In the following section, we present our methodology for investigating this question.

##  3\. Method

In this section, we present the methodology used to answer our research questions on the study of AI narratives in the arts. In particular, we present the procedure used to build the corpus of articles and their analysis.

###  3.1. Corpus creation

We created a corpus of press articles, news, blog posts on AI in the arts. We took these materials from several online media outlets. Some of them specialize in technology or in art, while others address the general public. Collecting such a corpus was challenging, as there is no centralized database for finding articles dealing with AI in the arts or AI in the creative sector. Our approach has been to place ourselves in the role of people asking questions about the issue and making queries on their search engines to gather information.

We started with two search engines that account for most queries in Europe and the USA, the parts of the world where this research was carried out. These search engines were Google and Microsoft Bing As request keywords we used: “[ Art AI |||| Artist AI |||| Creative AI ]” (where “||||” means “or”) paired with “[ articles |||| news |||| posts |||| innovations |||| products |||| companies ]”. This amounts to a total of 18 queries. The search was conducted systematically using both Bing and Google search APIs. Each query is specified to return responses whose date is between 2020 and Today. We chose to start in 2020 because we date the rise of generative AI, as it is understood today, to that year. This date also corresponds to the spread of generative AI in the creative and artistic sectors with the release of several relevant products, for instance, enabling image generation from text description.

As the list of search results to each query can be long (several hundreds of responses to each query), we chose to collect the first 90 search results for each query, which means the first 9 pages in the standard interface of these two search engines. We assume that the articles beyond the first 9 pages receive significantly less visibility. Finally, we merged all the query responses and removed the duplicated results. As a result, we collected 1720 query responses.

###  3.2. Corpus filtering and analysis

The corpus includes web pages from many sources. Figure [1](https://arxiv.org/html/2502.03940v1#S3.F1 "Figure 1 ‣ 3.2. Corpus filtering and analysis ‣ 3. Method ‣ Generative AI and Creative Work: Narratives, Values, and Impacts") depicts the number of articles published and the number of sources per month over the last four years, showing an increase in the number of publications correlated with an increase in the number of sources.

![Refer to caption](extracted/6183118/figure1.jpg) Figure 1. Bar chart of the number of publications per month obtained from our queries over the period January 2020 to the present date. The colours indicate the number of sources: a darker colour means a larger number of sources, while a lighter colour means a smaller number of sources.

In this work, our aim is to identify a subset of representative online web pages that we will analyse qualitatively. We opt for this approach in order to provide a more granular analysis of our research question on the representation of creative work in online media and by creative technology companies. First, we filtered the data set in order to retain a subset of web pages from sources that had published several articles on the topics over the years. We removed sources that were only occasionally published on the topic. We set the threshold at more than 5 articles over the 4 years. This threshold means more than one article per year (on average) and makes the list of final articles easier to handle for a qualitative analysis. In addition, we removed from the list academic publications and those related to tutorials on the use of AI tools in creative work. Finally, we included in the corpus websites from companies developing generative AI technologies for the creative and artistic fields. We extracted from these websites the general product descriptions. The final corpus comprises 188 articles from 19 sources plus 19 product descriptions. This corpus was analysed using inductive coding and thematic analysis. In this article, we present the part of the analysis relating to creative work. We produced a codebook containing 102 codes. These codes were then grouped into 5 themes that are presented in the following section.

##  4\. Findings

In this section, we present the results of our qualitative analysis. From the media outlets we identified five explicit values in the analysed narratives: Automation over manual work; efficiency over exploration; concept over execution; artifact over process; skills with a low barrier to entry over skills that take effort and time to learn. For each explicit value, we highlight the implicit narratives that underpin them and their impacts on creative work. Table 1 reports a summary of the findings, on which we elaborate below

![Refer to caption](extracted/6183118/figure2.jpg) Figure 2. Summary of the findings. We identified five explicit values in the analysis (first column), from which we exhibit their implicit narratives (second column) and impacts (third column)

###  4.1. Automation over manual work

A 2023 article published in Wired started with the following sentence: “It used to be widely thought that creative work would be one of the last things to be automated. After 2022, some may reconsider.” (Knight, [2023](https://arxiv.org/html/2502.03940v1#bib.bib38)) 2022 was the year where major text-to-image systems were released (Midjourney, Stable Diffusion, Dall-E, or Craiyon). These systems have been designed to automate the process that links the formalisation of a description in text to a visual rendering as an image: “The process of creating visuals is complex, manual and often requires specialized skills. [system] was created to address this challenge — providing a visual generative AI platform tailored to enterprises that digitizes and automates this entire process.” (Wiggers, [2023](https://arxiv.org/html/2502.03940v1#bib.bib64))

Certain artists such as Vera Molnar or Harold Cohen, mentioned above, used automation in their work. This was motivated by personal artistic gesture and aesthetics (Molnar, [1975](https://arxiv.org/html/2502.03940v1#bib.bib44)). With generative AI, the scale is not individual but global. The motivation behind the automation of the creative process through the use of generative AI does not stem from individual creative or artistic needs. Rather, automation is rooted in the capitalist pursuit of efficiency, productivity, and profit maximization and has been a driving force of industry since the third Industrial Revolution (Noble, [2017](https://arxiv.org/html/2502.03940v1#bib.bib49)). During the Industrial Revolution, mechanization replaced manual labor in factories, significantly reducing costs and increasing output (Mowery and Rosenberg, [1991](https://arxiv.org/html/2502.03940v1#bib.bib46)). This trend continued with the advent of electricity, assembly lines, and, more recently, digital technologies (Xu et al., [2018](https://arxiv.org/html/2502.03940v1#bib.bib68)). In today’s capitalist society, automation addresses the demand for faster production, and scalability in a globalized economy, while also reducing dependency on human labor. As put in a 2023 article published in FastCompany, “Generative AI tools are not artists; rather, they are creative factories. This is more like the industrial revolution for creativity. With these tools, creative efficiency is set to multiply astronomically.” (Clugston, [2023](https://arxiv.org/html/2502.03940v1#bib.bib17)). In turn, the narratives promulgated by AI-driven creative projects often highlight the technological marvel of the artistic medium (e.g., machine learning algorithms) and obscure the labor that goes into making, hacking, and adapting the algorithms to make them work for bespoke artistic contexts (Caramiaux et al., [2022](https://arxiv.org/html/2502.03940v1#bib.bib9)).

In addition, through the automation of the creative process, the underlying assumption is that creativity can be rationalised and streamlined such as becoming automated by an algorithm. Then, one of the dangers of this automation is the opacity of the underlying processes used to generate content, and the loss of artists’ autonomy in relation to their object of work.

###  4.2. Efficiency over exploration

There is a dominant discourse that privileges the notion of productivity efficiency in creative processes. The notion of efficiency primarily highlights time as a critical factor to be reduced during the creative process in the discourse shaping AI culture in the creative and artistic domains.

The depiction of creativity as a time-consuming, inefficient process is common in the narratives of companies developing AI-based technology solutions for artists and creatives. For instance, RunwayML, a company specialized in video editing, states that they “bring research into production within weeks instead of years.” Similarly, Captions, a company doing video storytelling, states that its “mission is to empower our customers, regardless of background or experience, with the ability to bring their vision to life in seconds.” Or AIVA, a European music generation company, proposes an assistant that “allows you to generate new songs in more than 250 different styles, in a matter of seconds.” The notion of efficiency implies that creating an artistic artifact or output involves a set of time-consuming tasks, and that those tasks can be performed and controlled by the AI-powered system instead of by the artist. The Canadian AI video creation company Lumen5 exemplifies this value proposition: “Video editing is a time-consuming process because it requires a lot of small tweaks and fine tuning. Using Machine Learning, we automate these tasks to help our users create quality videos with minimal time, effort, and training.”

This emphasis on efficiency cultivates the assumption that creative processes are not efficient enough (for the role assigned to them). In other words, standard metrics of productivity, such as quantity and speed, are equally valid in artistic spheres in order to assess the creative process. In doing so, technology providers state a problem that aligns conveniently with the technological solutions offered: they promote their products as necessary solutions to enhance efficiency and productivity. These companies may then be reinforcing an efficiency-focused framework that overlooks the nuanced needs of creative practitioners, shaping perceptions of the creative process to prioritize speed over exploration, mistakes and accidents. However, research has shown that artists value the latter even when engaging with AI: they often exploit the inherent unpredictability of AI algorithms, embracing chance and unexpected outcomes as part of the creative process (Caramiaux and Fdili Alaoui, [2022](https://arxiv.org/html/2502.03940v1#bib.bib11); Hertzmann, [2018](https://arxiv.org/html/2502.03940v1#bib.bib32)). This process is surely timely but it has also been shown to lead to the discovery of novel aesthetics, artistic styles, and forms of expression (Chang et al., [2023](https://arxiv.org/html/2502.03940v1#bib.bib14); Jiang et al., [2023](https://arxiv.org/html/2502.03940v1#bib.bib35)). Consequently, by encouraging efficiency, these narratives bring the very likely prospect of discouraging artists from exploring, while increasing the pressure on production. As artist Mario Klingemann put it: “Rather than contributing more to the ever-growing pile of ‘content’, artists might need to create less. This seems counterintuitive, but in a world saturated with AI-generated output, the artist might shift towards curation, distillation, and extracting meaning from the noise.” (Silva, [2024](https://arxiv.org/html/2502.03940v1#bib.bib58)).

###  4.3. Concept over execution

AI technologies aimed at artists, including text-to-image and text-to-video systems, place a strong emphasis on the role of conceptualization in the creative process, suggesting that a compelling idea holds greater value than its material realisation: the technical execution of that idea, or the embodied actions made to realize it. There is a dominant narrative that creativity can be let free through a streamlined path from the creator’s idea to the final artifact, be it an image, a sound, a video, a text, etc. For instance, Boomy (music generation), Leonardo.AI (image generation), Synthesia (video creation), all use the metaphor of creativity being constrained and that their products would help to “unleash”. “Let your creativity flow, and make AI do the rest” is the marketing slogan of Descript, which specializes in video editing.

Within the framework of generative AI, the stance is that creation occurs as a direct transformation from concept to artifact (output, or object). This process involves a disembodied approach to creativity, in which the importance of the mind and abstract conceptualization is put forward, over other aspects of production. This emphasis suggests that the creative act is about ideation and can be limited or constrained by material engagement. “AI cultivates and caters to our passivity, seeming to offer the fruits of creativity and self-examination without the effort and self-doubt.” (Horning, [2022](https://arxiv.org/html/2502.03940v1#bib.bib33)) The idea is that all an artist has to do to make a creation is prompting, i.e., writing a sequence of words that defines a command for the generation algorithm.

Commentators have linked the importance of the concept and the idea in the creative process to the postmodern and surrealist currents in art history. “A.I.’s use for rendering images follows a postmodern tradition in art, in which the final product is defined less by skill and more by the theory it posits” (Pilat, [2022](https://arxiv.org/html/2502.03940v1#bib.bib53)). Surrealists were using methods based on algorithms, chance, meditation methods, etc. These methods aimed at freeing themselves from a normative rationalism (Dafoe, [2024](https://arxiv.org/html/2502.03940v1#bib.bib18)). Paradoxically, outsourcing a significant proportion of creative decisions to AI can lead, on the contrary, to the artist being constrained by the (unseen) current and past cultural motifs represented in AI training data, without going beyond them. As Ted Chiang explains, “If an A.I. generates a ten-thousand-word story based on your prompt, it has to fill in for all of the choices that you are not making. There are various ways it can do this. One is to take advantage of the choices that other writers have made, as represented by text found on the Internet; that average is equivalent to the least interesting choices possible […]. Another is to instruct the program to engage in style mimicry, emulating the choices made by a specific writer, which produces a highly derivative story. In neither case is it creating interesting art.” (Chiang, [2024](https://arxiv.org/html/2502.03940v1#bib.bib15))

Delegating “small-scale” tasks to AI might not be an arbitrary framing. Companies emphasizing efficiency assume that these types of tasks are uninteresting or simply “busy work.” Yet these tasks might be considered by creatives as crucial elements of the creative process. Chiang argues exactly this point when he states that “…art requires making choices at every scale; the countless small-scale choices made during implementation are just as important to the final product as the few large-scale choices made during the conception. It is a mistake to equate ‘large-scale’ with ‘important’ when it comes to the choices made when creating art; the interrelationship between the large scale and the small scale is where the artistry lies.” (Chiang, [2024](https://arxiv.org/html/2502.03940v1#bib.bib15)) The inclination of considering small adjustments, especially those that involve repetitive actions, as less significant within the broader creative process may stem from a cultural hierarchy that places higher esteem on the conceptual and unique aspects of art, as in postmodernism, while undervaluing the technical precision and skill associated with craftsmanship. As Anna Ridler, conceptual artist working with AI put it: “I actually think that there are a lot of parallels between craft vs art and dataset vs algorithmic output. I think there are a couple of things like the relation between craft which is anonymous and less well regarded, and how it’s repetitive, versus art.” (Caramiaux and Fdili Alaoui, [2022](https://arxiv.org/html/2502.03940v1#bib.bib11)) In AI art, Ridler explicitly exhibits the craft of her work by exhibiting the dataset she created, made of thousands of images, alongside with the model’s outputs, generated from this dataset.

One of the dangers we see in the emphasis of concept over execution is a reduction in the diversity of content created, since a significant number of elements and details will be left to the decision of the generative model. This phenomenon starts to be documented (Doshi and Hauser, [2024](https://arxiv.org/html/2502.03940v1#bib.bib22)) and can lead to stylistic exhaustion: “culture is going nowhere fast. The worries that accompanied artificial intelligence in 2023 reaffirmed this fear: that we’ve lost something vital between our screens and our databases, that content has conquered form and novelty has had its day. If our culture has grown static, then might we call our dissembling chatbots and insta-kitsch image engines what they are: mirrors of our diminished expectations?” (Farago, [2023](https://arxiv.org/html/2502.03940v1#bib.bib25)).

###  4.4. Artefact over process

In the narratives that we analyzed, efficiency and productivity are understood as added value, which implies a specific definition of “value” based on the quantity of production rather than the quality of production and artistic experience and growth. Saving time increases productivity, i.e., the quantity of “things” produced by creative people and artists. As it is stated in a recent article published in Forbes, “For businesses, this will mean quicker project completion, while freelancers will fit more work into their schedules, allowing them to take on more work or offer lower prices to their clients” (Marr, [2024](https://arxiv.org/html/2502.03940v1#bib.bib41)).

While this logic of increased supply driving down prices aligns with principles of capitalist economies, where competition and increase of offerings typically lead to lower costs, this dynamic raises significant questions regarding its desirability within the creative and artistic sectors. Creative outputs are significantly subjective and require time for ideation and refinement, aspects that are at risk in a model that values volume and speed over intrinsic quality. The Creative Economy already relies heavily on freelance and gig-based work, work that is marked by inconsistent income, limited protections, and potentially limited benefits (Taylor and Luckman, [2020](https://arxiv.org/html/2502.03940v1#bib.bib61)). The artistic labor market is known to attract more aspirant artists than the available jobs (Menger, [1999](https://arxiv.org/html/2502.03940v1#bib.bib43)) partly due to an overestimation of the chance of success. The rise of AI in the creative sector may create higher incentives for aspiring artists, who could perceive careers traditionally characterized by autonomy and freedom as more accessible than ever before. This shift aligns with the increase of flexible art-related careers, where specialization may give way to adaptability across diverse opportunities (Lingo and Tepper, [2013](https://arxiv.org/html/2502.03940v1#bib.bib40)). The application of generative AI to creative and artistic work could further accelerate this trend, fostering a professional landscape that values versatility over narrow expertise.

One of the consequences with these new AI advancements is also the risk to see generative AI establish new lower standards for quality, time to deliver, and pricing. As AI tools continue to evolve, they offer rapid, nearly free or cost-effective alternatives for tasks traditionally handled by creatives and that usually take time (as presented previously). This shift may redefine client expectations, prioritizing faster turnaround times and lower costs, which could marginalize creatives whose practices do not integrate easily with AI technologies or who lack proficiency in using these tools. For these individuals, staying competitive becomes increasingly challenging, as they may struggle to meet new industry benchmarks. Creatives who rely on traditional, manual, or non-AI-compatible methods may then find it difficult to follow their careers in an environment increasingly dominated by AI-driven production. This technological divide risks deepening inequalities within the creative sector. As artist Rubem Robierb stated in a CNBC article: the use of AI is “not a matter of choice” (Handley, [2024](https://arxiv.org/html/2502.03940v1#bib.bib29)).

###  4.5. Short-term over long-term skills

One reason behind the focus on ideation over execution is to reduce the need to acquire time-consuming technical skills, which in return brings more people to use the tool. “I [Picsart’s CEO] really see it more like a copilot to allow humans to be even more creative and more advanced in their expression. So the things which were taking years of learning and training and fine-tuning, now you can do it easier and faster. It’s opened an opportunity for more people to be creative.” (Snelling, [2023](https://arxiv.org/html/2502.03940v1#bib.bib60)) The creative process was seen as something previously reserved for artists, for those who had learned the technical skills to create. “Most of these users are not […] professional artists, and that’s the point: They do not have to be.” (Kelly, [2022](https://arxiv.org/html/2502.03940v1#bib.bib37))

The idea that it is now possible to create visual or musical works of art without any specific skills in drawing, color, or composition is being promulgated by many companies. For instance, Boomy proposes to “create original songs in seconds, even if you’ve never made music before.” Similarly, Lumen5, specialized in video creation, states that its “goal is to enable anyone without training or experience to easily create amazing videos in minutes.” As a final example, Photoroom, specialized in photo editing, “provides photo editing software powerful enough to create outstanding images yet simple enough to be used without any training.” These examples illustrate the underlying idea that it’s no longer worth spending time learning the skills required for drawing, video, or musical composition. The value is in the product.

Outsourcing important elements of the creative process begs the question of what skills artists retain or what skills they discard. AI is depicted as contributing to the “escalation of creativity” where “anyone can write at the level of Shakespeare, compose music with Bach, [and] paint in the style of Van Gogh” (Pieters and Winiger, [2016](https://arxiv.org/html/2502.03940v1#bib.bib52)). World-renowned institutions such as the World Economic Forum have also published reports on the impact of AI in the Creative sector (World Economic Forum, [2018](https://arxiv.org/html/2502.03940v1#bib.bib66)), detailing how AI will slowly manage to perform increasingly complex creative tasks reserved for humans until now. The driving assumption in these projects is that algorithms can encapsulate creative knowledge and that by having access to suitable algorithms, artistic expertise can and will naturally emerge.

These emerging narratives about the benefits of outsourcing expertise to generative AI echo the ones surrounding the global outsourcing of labor in the 1990s. Both sets of stories are grounded on the pursuit of efficiency, cost reduction, and scalability, often at the expense of traditional job security. In the 1990s, companies relocated manufacturing and service jobs to countries with lower labor costs, triggering debates about job displacement, economic inequality, and the erosion of local expertise. Similarly, today’s adoption of AI for tasks that previously required hard-earned skills do raise concerns about skill devaluation, and the ethical implications of automation.

Another vision is to take creative work to a macroscopic, generalist level, where artists would not have specific technical skills but would be artistic directors injecting ideas and their cultures into the automated process. “The next generation of artists and creatives will be closer to generalists. They’ll understand various aspects of art, anthropology, history, sociology, and music and use these broad cultural insights to guide the unrivaled dot-connecting-capacity of AI.” (Clugston, [2023](https://arxiv.org/html/2502.03940v1#bib.bib17)). This last statement raises the question of future artist training. If technical skills lose their value, the training that enables them to be acquired is likely to decline.

##  5\. Discussion

In this article, we have analyzed the dominant narratives around AI in the creative domain, focusing on the values that these narratives convey about creative work. We found that these narratives are anchored in five explicit values, which are: automation over manual work, efficiency over exploration, concept over execution, artefact over process, short-term over long-term skills. And each value embeds implicit narratives about what is creative work under the eye of generative AI and those who develop the technology. We believe that the impact of these narratives is not without consequences for the creative and artistic sectors, and that it is therefore important to analyze them in order to respond to them.

First, the narratives surrounding generative AI in creative fields don’t necessarily present it as generating a decrease in creative work, but rather highlight its potential to increase and expand creative possibilities. The emphasis on automation and efficiency is aimed at empowering creatives by enabling them to focus on tasks considered rewarding. We believe that these narratives highlight generative AI as a tool for empowerment because they are rooted in a tradition where creativity is seen as problem-solving, suggesting that creative work follows rational and systematic processes that can be handled by generative AI (Hsueh et al., [2024](https://arxiv.org/html/2502.03940v1#bib.bib34)). This standpoint finds its root in the foundations of AI, as formalized by Simon: “I will try to show that there is no real boundary between WSPs [well-structured problems] and ISPs [ill-structured problems], and no reason to think that new and hitherto unknown types of problem solving processes are needed to enable artificial intelligence systems to solve problems that are ill structured.” (Simon, [1973](https://arxiv.org/html/2502.03940v1#bib.bib59)). If creativity is seen as problem-solving, with its underlying processes that can be automated, generative AI becomes a valid tool and medium to assist creative practitioners and artists. However, the harms to creative work highlighted in this article, together with other criticisms made in recent research (e.g. (Jiang et al., [2023](https://arxiv.org/html/2502.03940v1#bib.bib35))), show that creative work cannot simply be seen as problem solving and this view more certainly engenders problem-making solutions.

Second, the notions of empowerment and democratization, highlighted in the narratives analyzed, have a long tradition as rhetorical means in the sphere of digital technologies (see for instance Turner’s work on cyberculture (Turner, [2010](https://arxiv.org/html/2502.03940v1#bib.bib62))). However, tools that are said to be designed to promote inclusion and access may in fact reinforce existing inequalities or serve specific power structures (Wijesiriwardena, [2017](https://arxiv.org/html/2502.03940v1#bib.bib65)). In the early days of the Internet, researchers like Lisa Nakamura demonstrated the performativity of identities and stereotypes in spheres of online discussion, promoting radically democratic forms of discourse in which bodies and race do not intervene (Nakamura, [1995](https://arxiv.org/html/2502.03940v1#bib.bib47)). About digital divide, Alondra Nelson put: “Blackness gets constructed as always oppositional to technologically driven chronicles of progress.” (Nelson, [2002](https://arxiv.org/html/2502.03940v1#bib.bib48)) In the creative field, tools are positioned as democratizing by the removal of specific skills and expertise that used to be necessary for creation, such as drawing, composition, visual literacy, and so on. We have shown that this framework can put additional tension on artists’ already precarious careers, and lead to a decline in support for artistic training. This use of “democratization” could deny creative and artistic careers to people who are not in a privileged position, and thus reinforce the exclusivity of access to these careers for an even more restricted part of those concerned.

Third, the discourse around generative AI in the media outlets that we analyzed come from a majority of non-artists. Artists and creative practitioners are generally absent from the discourse, and their expertise is not always clearly represented in the communication surrounding the objects of their expertise. There are exceptions, such as Ted Chiang’s article in The New Yorker (Chiang, [2024](https://arxiv.org/html/2502.03940v1#bib.bib15)). However, the vast majority of artists do not have access to these media. In this context, which marginalizes artists’ perspectives, we see another problematic effect, which is the invisibilization of the community. When the community is eclipsed, the focus shifts to individual users as the primary targets of these tools. Such an individualist view sees creatives as having desires regarding their tools, which can be taken in isolation of social influence (echoing Dewey’s description of individualism (Dewey, [1934](https://arxiv.org/html/2502.03940v1#bib.bib20))). Paradoxically, the design paradigms involved in generative AI, such as using natural language as inputs to creation, do not stem from artists’ practice and desires (Epstein et al., [2023](https://arxiv.org/html/2502.03940v1#bib.bib23); McCormack et al., [2023](https://arxiv.org/html/2502.03940v1#bib.bib42)). These design choices lower the entry barriers for a greater number of users who are neither experts nor artists, but amateur and apsiring artists.

Finally, our work has its limitations. As technology evolves rapidly, we might wonder whether narratives don’t evolve too. For example, we believe that five years ago, narratives of generative AI in art emphasized the replacement of artists by technology, whereas current narratives focus more on augmentation and collaboration. Then, in this work, we have selected a subset of articles in order to conduct a qualitative analysis that is more granular and driven by our research questions than computational approaches. The process is therefore not systematic and calls on our subjectivity. This is both a strength and a limitation of the method.

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