AI art is getting a lot of controversy for its implications for current artists. What will it do to employment prospects in the arts? What about the copyright implications? What about all of the art that is used to train these models?
All of those questions are important things to think about.
I for one think that some of the fears of human artists getting fully displaced by automation is a bit over stated.
I think it won’t displace artists as much as people are worried about. I think it will be just another tool that’s used to create art.
It may reshuffle the decks a bit and maybe put some people out of business, put some people into business, but it won’t be as much of a sea change in that regard as many think.
However, what worries me the most about the increasing role of AI tools is their closed nature. As these increasingly sophisticated AI models do more and more, not just in the field of art but in every aspect of our lives, it’s crucial that these tools are open and accessible to everyone.
Unfortunately, that is not the case with most of these tools. Currently, the AI models and their outputs and inputs are owned by just a few companies, leaving most users locked out.
I have a strong concern that this will concentrate the art market, displacing the decentralized infrastructure and ecosystem of small business artists with a much more centralized art world, dominated by a few companies that provide tools that play an increasingly critical role in creating art in the modern world.
The majority of the significant recent generative AI models are proprietary, from AI music generators to tools like GPT and MidJourney. These tools are not even available for use on your own computer, instead, you have to send your inputs to be processed on a cloud server owned and maintained by the authors of the AI model. Even a few models that are source available (and even marketed as open source), like Stable Diffusion, are not fully free and open source.
One reason for these models not being free and open source is what some sources call “toxic candy models.”
As per this memo by a contributor to the Debian Linux distribution, writing in regards to determining which AI software should be included as FOSS, these are models where the algorithm’s weights and other parts are a complete black box, and you only receive the final output of the model generation process without information on how it was generated.
This includes models based on data/input scraped from the internet. This results in situations where the art used to create the final model is usually proprietary, and the legality of even doing this scraping is in dispute. And of course the companies can’t distribute that art to anyone who wants to modify or fully understand the model. They can’t provide a full list of every bit of art they used.
So you as a user, if you want to fundamentally modify what’s fed into those models, if you want to see what’s fed into those models and figure out where the model gets what it gets from, you fundamentally can’t under the current ecosystem.
Access to the data is necessary for users to fully understand, modify, use, and build own their versions of the model based on this.
I think that this issue is adjacent to one of the more plausible arguments that the models should be considered a derivative work of the input art – but I am not sure if I endorse such an argument.
A concerning trend is for companies producing source-available AI models to release them under non-free and open-source licenses that do not meet standard guidelines for open-source licenses, such as the FSF definition, the OSI open source definition, or the Debian Free Software Definition.
The most notable of these licenses is the Responsible Artificial Intelligence Source Code (RAIL) License, which imposes restrictions on how users can use the output generated by the tool.
This is similar to proprietary companies that claim copyright interest in the output of their program
This is a departure from the open-source community’s consensus that the software developer does not have ownership over what people use the software for – despite the fact that some of the companies involved still attempt to claim to be open source friendly.
There is a movement in the software industry, particularly in the AI world, for developers to dictate what users can do with their software.
This mindset and movement asserts that the developer of the software has, in terms of both moral obligation and right, and in terms of the legal ability to enforce this, has the duty and ability to basically dictate what users do with this software.
https://facctconference.org/static/pdfs_2022/facct22-63.pdf
(From the Abstract) A number of organizations have expressed concerns about the inappropriate or irresponsible use of AI and have proposed ethical guidelines around the application of such systems. While such guidelines can help set norms and shape policy, they are not easily enforceable. In this paper, we advocate the use of licensing to enable legally enforceable behavioral use conditions on software and code and provide several case studies that demonstrate the feasibility of behavioral use licensing. ```
From Pg 4 In this paper, we seek to encourage entities and individuals who create AI tools and applications, to leverage the existing IP license approach to restrict the downstream use of their tools and applications (i.e., their “IP”). Specifically, IP licensors should allow others to use their IP only if such licensees agree to use the IP in ways that are appropriate for the IP being licensed. While contractual arrangements are not the only means to encourage appropriate behaviour, it is a mechanism that exists today, is malleable to different circumstances and technologies, and acts as a strong signaling mechanism that the IP owner takes their ethical responsibilities seriously. ```
This has the potential to spread beyond the AI world and impact the norms of the software industry as a whole. This mindset is in blatant contradiction of not just the norms of the open source community, but also the old norms of the software industry as a whole.
The expansion of copyright for AI technology is a big concern. The RAIL license, used by Stable Diffusion among others, is an interesting and notable case.
The developers behind this license believe it is necessary to prevent harmful and irresponsible uses of their products, and they believe that AI technology has a lot of potential for misuse. They argue for the need to come up with a legally enforceable mechanism to limit potentially irresponsible uses.
https://www.licenses.ai/
Responsible AI Licenses (RAIL) empower developers to restrict the use of their AI technology in order to prevent irresponsible and harmful applications. These licenses include behavioral-use clauses which grant permissions for specific use-cases and/or restrict certain use-cases. In case a license permits derivative works, RAIL Licenses also require that the use of any downstream derivatives (including use, modification, redistribution, repackaging) of the licensed artificial must abide by the behavioral-use restrictions.
But I don’t particularly agree with the necessity of using copyright as a means for this.
However, I do not agree with the use of copyright as a means to achieve this.
AI art generation may do different things than traditional art methods, but it’s not as much of a game-changer as some people claim. AI is just a buzzword for things that seem computationally practical based on everyday experiences, but where practical algorithms are new or nonexistent. Today’s AI techniques will become tomorrow’s conventional art techniques, and software tools for modifying and creating art have existed for a long time, such as Photoshop and GIMP. These AI tools are just an extension of digital art.
Artistic controversies, such as whether or not something is real art, have arisen before with new forms of art, such as photography. AI art is just another method of art that uses technology to probe and sample an extrinsic space outside of the artist’s mind, similar to how photography creates art by sampling from from the physical environment.
In both cases, the artist’s creativity comes from knowing what to sample, and how to sample it, thereby the creation of novel art is possible.
Both conventional art methods and the new AI art have a lot of the same ethical issues.
For example, one that gets mentioned a lot is the ability of AI art to potentially create fake media. Images that look like they’re of a real person or of a real event, but aren’t actually representative of the world.
However, traditional means for visual art also have lots of ways to be misleading, manipulated, edited, and staged in a way that doesn’t reflect the real world. People often overestimate how accurate visual arts, especially photographic arts, are at truly representing the world. The new technology driving new ways to manipulate and generate imagery may reset the social environment around visual arts to something that’s actually more healthy and representative of not just what AI art is, but what visual art has always been.
The extreme (but unlikely) case might be when fakes become so common that the only way to trust an image is to know where it came from, its history. This would reduce visual imagery to how it was perceived before modern photography became widely available, in which you had to trust the testimony of the artist or the author for wherever you were getting the image from.
Every medium of art or expression has the ability to mislead and be misused, and the mechanisms that society has to limit that misuse don’t need to change with this new technology. The needed legal mechanisms already exist, such as defamation law, to limit the use of faked images to lie about someone. Attempting to bring copyright into what’s been traditionally handled by defamation law is an attempt to rewrite the balance. Copyright has different, often more extreme penalties, than society has seen fit to impose for conventionally.
And how society has deemed it proper to handle things like lying about people or deliberately misleading has been constructed by the process of democracy and centuries of societal experience, to optimize various societal trade-offs. To balance negative social effects of potentially dangerous content and or damage to people’s reputation, disseminating, versus the importance of freedom of expression.
This type of rulemaking is fundamentally anti-democratic and technocratic, as it appoints those who write the license and push the rules as arbiters of how society should handle these risks. It also doesn’t take into account the ways in which humans can fail, sometimes more than machines can fail. For example, traditional human forensic methodologies can also be very inaccurate, yet still entered as evidence.
The use of AI technology raises many important questions about its potential misuse and accountability.
But it is not necessarily true that AI technology is worse than humans in many cases often discussed.
For instance, consider the process of creating a sketch of a suspect. A witness description could be interpreted by a human sketch artist or an AI model, both of which are interpretations and not the ground truth. The AI system may even come up with an equal or better interpretation than the human.
It is crucial to have a wide social debate about the trade-offs of AI and where its limits lie. When is AI better than humans, and when has society already gone too far in trusting human methods? AI has many of the same limitations as humans, but it may demonstrate those limits in a way that prompts society to reconsider its past decisions and to be more responsible with both human and automated decision making.
There is also the issue of accountability, especially when it comes to the normal legal system. A top-down institutional approach to limiting technology has much less accountability to the public and lacks a wide range of perspectives, leading to less legitimate and often worse results.
I believe this mindset could spread throughout the software industry, including to places where it would be very dangerous.
If this idea of social responsibility of companies and developers to restrict their users becomes more widespread, it would rewrite the balance of power between software companies and consumers in favor of the companies.
Imagine if this mindset is taken to conventional tools. Imagine the world in which Microsoft is treated as both in terms of legal power and in terms of generally perceived ethical responsibility as responsible for what a writer does with Microsoft Word. Or if Adobe is considered in the same way responsible for what an artist does with Photoshop or Illustrator and so on.
It would be no longer a world where you can do what you want with a piece of software that runs on your computer. Someone else, someone with limited accountability to you, would have a lot more power over what you can do on your own computer.
The companies who make the software you use would have more power over what you can do with their software, and this change could make the world a much worse place.
A point raised in the previously linked discussion of responsible AI licenses is the idea of authorial integrity over software. The developer or the company who produced it holds the mindset and vision that should influence what users do with the software. It is contended that this artistic or authorial vision should also affect everyone downstream. However, using the software in a way that is not part of that vision is essentially violating the rights of the author or the developer or the company.
https://facctconference.org/static/pdfs_2022/facct22-63.pdf
(Pg 2) The context in which a model is applied can be far removed from that which the developers had intended, a major point of concern from the perspective of human-centered machine learning [31] … applications that may be of concern, such as large-scale surveillance or the creation of “fake” media. In some cases, the developers or technology creators may legitimately want to control the use of their work due to concerns arising out of the data that it was trained on, the technology’s underlying assumptions about deploy-time characteristics, or the lack of sufficient adversarial testing and testing for bias. This is especially true of AI models that are difficult or expensive to recreate. For example, given that models such as GPT-3 [17] reportedly cost over $10 million (U.S.) to train, very few organizations are positioned to train (and potentially, need to retrain) a model of similar size
The mindset that the developer or the company has control over the software is incorrect. There is a big difference between functional works and creative works, and software falls into the category of functional works. Software is essentially a description of a process and a set of instructions, a tool that is used to guide a method.
It’s like a recipe or a textbook telling how you need to mix the paints to get a color. It’s not the painting that uses that color.
Control over the software used to make art, is fundamentally exerting control over a method, over a technique that’s represented by that software.
A work of art is a final product that can stand on its own, a work that’s enjoyed by itself. In that case, an artist can have an actual creative vision that’s put through into their art. And I think that doesn’t work when you get a tool like software.
The paper raises the cost of creating the software as a reason for preserving the vision, but I believe that considering the cost of software development moves things in the opposite direction.
In the art world, there is potential for substitutes, for other artists to come in and make a work of art that reflects their vision without necessarily needing to modify or use what another artist has done. The resources available to make art are often common enough or inexpensive enough that many visions of what art should be can coexist with each other. You can have many artists creating many works, and each of those works with their own vision.
But when you get a software program that costs tens of millions of dollars, even hundreds of millions of dollars to produce, a normal person can’t step into that competition, they can’t step into that creative process around developing software.
With software, the cost of production is so high that a normal person cannot compete in the creative process of developing software, giving the developer or copyright holder a lot of power over society.
Once you include the case of interlinked supply chains, programs that are dependent on other programs, and the entire tech stack would have to be rebuilt from the ground up to have a different vision, which is infeasible even for the wealthiest person on the planet.
This is why the freedom to use and modify software and expand upon it is important and critical. Asserting that copyright holders or companies or software developers have the right or obligation to restrict its use is very dangerous.
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