The Rise of Generative AI: Exploring Questions of Ownership and Authorship
Summary:
In 2022, an AI-generated artwork won the Colorado State Fair art competition. The artist, Jason Allen, used a generative AI system called Midjourney to create the piece. However, this sparked backlash and raised questions about ownership and authorship in the realm of generative AI. Should artists be compensated for their art that was used to train AI models? Who owns the images that AI systems produce? Is fine-tuning prompts for generative AI a form of authentic creative expression?
A team of 14 experts from different disciplines recently published an article in Science magazine, exploring how advancements in AI will impact creative work, aesthetics, and media. One key question that arises is whether US Copyright Laws can adequately address the unique challenges of generative AI. Copyright laws were created to promote the arts and creative thinking, but the emergence of generative AI has complicated existing notions of authorship.
The article draws a parallel to the emergence of photography in the 19th century. Initially, many argued that photography lacked artistic merit. However, the US Supreme Court ruled that photographers should own the photos they create, as cameras serve as tools for artists to give visible form to their ideas. Over time, photography developed into an art form of its own.
Unlike inanimate cameras, AI has capabilities that make it vulnerable to anthropomorphization, leading to questions about who can claim ownership of images created by AI. The US Copyright Office has stated that only humans can own copyrights. This raises the question of who should own the images created by AI: the artists whose images were used to train the systems, the users typing in prompts, or the people who build the AI systems.
The use of training data in generative AI raises concerns about copyright infringement and fair use. Training data often consists of copyrighted works, collected without the knowledge or consent of the artists. Artists using generative AI tools go through rounds of revisions to refine their prompts, suggesting a level of originality. The article raises questions about who should own the results and explores the contributions of everyone involved in the generative AI supply chain.
Legal analysis becomes easier when the output differs from the training data. If the results are similar to works in the training data, questions of copyright infringement may arise. The article mentions examples where an artist’s unique style was mimicked by generative AI, raising questions about ownership of the AI-generated work.
The article suggests that existing copyright laws may need to be interpreted or reformed to address the unique challenges of generative AI. Some scholars propose new rules to protect and compensate artists whose work is used for education, such as the right to object to the use of their data or automatic compensation. Co-ownership models have also been proposed, allowing artists to obtain some rights to results similar to their works.
Ultimately, how existing laws are interpreted or reformed and how generative AI is treated as a tool will have significant consequences for the future of creative expression.
Additional Piece:
Generative AI: Balancing Innovation and Creativity
Generative AI has undoubtedly opened up new possibilities for artists and creators, providing them with powerful tools to explore and express their ideas. However, as with any emerging technology, it comes with its own set of challenges and controversies. The question of ownership and authorship in the realm of generative AI is one such challenge that needs careful consideration.
On one hand, there are proponents of generative AI who argue that it is a revolutionary tool that pushes the boundaries of creativity. They view it as a way for artists to collaborate with AI systems, expanding their creative potential and exploring new artistic territories. Jason Allen, the artist who won the Colorado State Fair art competition with his AI-generated artwork, is an example of someone who sees generative AI as a tool that enhances his artistic process.
On the other hand, there are concerns that generative AI may undermine the traditional role of the artist and devalue their work. Some argue that using the art of others as training data without proper compensation or consent is exploitative and raises ethical questions. The AI systems themselves have no awareness or intention, leading to debates about who should be credited as the creator of AI-generated works.
To address these concerns, there is a need for a balanced approach that takes into account the interests of all stakeholders involved. Artists whose work is used as training data should have the right to object to its use or be compensated for their contribution. Users of generative AI tools should also be aware of copyright infringement and respect the creative work of others.
Additionally, there is a need for clearer guidelines and regulations surrounding generative AI. Copyright laws may need to be updated to address the unique challenges posed by this technology. Co-ownership models, as proposed by scholars, could provide a fair solution that recognizes the contributions of both artists and AI systems.
Ultimately, the goal should be to foster innovation and creativity while ensuring that the rights and interests of artists are protected. Generative AI has the potential to be a powerful tool for artistic expression, but its development must be guided by principles of fairness, respect, and accountability. By addressing the questions of ownership and authorship in a thoughtful and inclusive manner, we can navigate the possibilities and challenges of generative AI in a way that benefits artists, creators, and society as a whole.
Summary:
Generative AI has sparked debates about ownership and authorship in the arts. Questions arise about compensation for artists whose work is used to train AI models and who owns the images generated by AI. Scholars are exploring how US Copyright Laws can address the unique challenges of generative AI. The emergence of photography was also met with skepticism, but it evolved into its own art form. AI is different from cameras, as it can replicate and mimic styles, leading to questions of ownership. Copyright infringement and fair use become relevant when using training data, and questions arise about who should own the results. There are proposals for protecting and compensating artists and exploring co-ownership models. The interpretation and reform of existing laws will have significant implications for the future of generative AI and creative expression.
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In 2022, an AI-generated artwork won the Colorado State Fair art competition. Artist Jason Allen had used Midjourney – a generative AI system trained on art scraped from the internet – to create the piece. The process was far from fully automated: Allen went through about 900 iterations over 80 hours to create and refine his contribution.
But his use of AI to win the art competition sparked backlash across the internet. with a Twitter user claimed“We are watching the death of art unfold right before our eyes.”
As generative AI art tools like Midjourney and Stable Diffusion have come under the spotlight, questions about ownership and authorship are also emerging.
The generative ability of these tools is the result of training on dozens of previous artworks, from which the AI learns how to create artistic results.
Should the artists be compensated whose art was scraped to train the models? Who owns the images that AI systems produce? Is the process of fine-tuning prompts for generative AI a form of? authentic creative expression?
On the one hand, Technology enthusiasts rave Revision as with Allen. On the other hand, many working artists find it useful to use their art to train AI exploitative.
We are part of a team of 14 experts from different disciplines who just published an article on Generative AI in Science magazine. In it, we explore how advancements in AI are made will impact creative work, aesthetics and media. One of the key questions that arose has to do with that US Copyright Lawsand whether they can adequately address the unique challenges of generative AI.
Copyright laws were created to promote the arts and creative thinking. But the rise of generative AI has complicated existing notions of authorship.
Photography serves as a helpful lens
Generative AI may seem unprecedented, but history can serve as a guide.
Take the Emergence of photography in the 19th century. Before its invention, artists could only attempt to depict the world through drawing, painting, or sculpture. Suddenly, with the help of a camera and chemicals, reality could be captured at lightning speed.
As with generative AI, many argued that photography lacked artistic merit. In 1884 the The US Supreme Court ruled on the matter and found that cameras served as tools by which an artist could give visible form to an idea; The court ruled that the “masterminds” behind the cameras should own the photos they created.
From there, photography developed into an art form of its own and even started a spark new abstract artistic movements.
AI cannot own expenses
Unlike inanimate cameras, AI has capabilities—like the ability to turn basic instructions into stunning works of art—that make it what it is vulnerable to anthropomorphization. The very term “artificial intelligence” leads people to believe that these systems have human-like intentions or even self-awareness.
This has led some people to question whether AI systems can be “owned”. But the US Copyright Office has stated this in no uncertain terms Only humans can own copyrights.
So who can claim ownership of images created by AI? Is it the artists whose images were used to train the systems? The users typing in prompts to create images? Or the people who build the AI systems?
Violation or Fair Use?
While artists indirectly draw on previous works that formed and inspired them to create, generative AI relies on training data to produce results.
This training data is prior artwork, much of which is copyrighted, and was collected without the knowledge or consent of the artist. Such use of art could infringe copyright even before the AI generates a new work.
Was that a form of infraction? Or was it a new form of “fair use“A jurisprudence that permits the unlicensed use of protected works when they have been sufficiently transformed into something new?
While AI systems don’t contain literal copies of the training data, they do sometimes manage to replicate works from the training data, which complicates this legal analysis.
Will contemporary copyright law favor end users and companies over the artists whose content is contained in the training data?
To address these concerns, some scholars are proposing new rules to protect and compensate artists whose work is used for education. These proposals include a right for artists object to the use of their data for generative AI or a way to do it Automatically compensate artists when their work is used to train an AI.
However, training data is only part of the process. Artists using generative AI tools often go through many rounds of revisions to refine their prompts, suggesting a level of originality.
To answer the question of who should own the results, the contributions of everyone involved in the generative AI supply chain must be examined.
Legal analysis is easier when an output differs from the works in the training data. In this case, whoever caused the AI to generate the output appears to be the default owner.
However, copyright law requires meaningful creative input – a standard that is met by clicking a camera’s shutter button. It remains unclear how courts will decide what this means for the use of generative AI. Is it enough to compose and refine a command prompt?
It gets more complicated when the results are similar to what was done in the training data. If the similarity is based only on general style or content, it is unlikely to infringe copyright since the style is not copyrighted.
Illustrator Hollie Mengert encountered this problem firsthand when her unique style was mimicked by generative AI engines in a way that didn’t capture what she thought was the case. made her work unique. Meanwhile, singer Grimes has embraced technology, “open-sourced” her voice, and encouraged fans to create songs in their style with generative AI.
If an output contains essential elements of a work in the training data, it may constitute copyright infringement of that work. The Supreme Court recently ruled that Andy Warhol’s drawing of a photograph was not allowed by fair use. This means that using AI to simply change the style of a work—say, from a photograph to an illustration—is not enough to claim ownership of the altered edition.
While copyright law tends to advocate an all-or-nothing approach, Harvard Law School scholars have proposed new models for doing so co-ownership which allow artists to obtain some rights to results similar to their works.
In many ways, generative AI is another creative tool that will give a new group of people access to image generation, just like cameras, paintbrushes, etc Adobe Photoshop. A key difference, however, is that these new tools are explicitly based on training data, so creative contributions cannot be easily traced back to a single artist.
How existing laws are interpreted or reformed – and whether generative AI is treated appropriately for the tool it is – will have real consequences for the future of creative expression.
Jessica Fieldlaw lecturer, Harvard Law School,
Ziv EpsteinPhD student in Media Studies and Studies, Massachusetts Institute of Technology (MIT)
https://fortune.com/2023/06/16/generative-a-i-copyright-law/
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