Tag Archives: Artificial Intelligence

What happens when your publisher licenses your work for AI training?

Posted July 30, 2024

Over the last year, we’ve seen a number of major deals inked between companies like OpenAI and news publishers. In July 2023, OpenAI entered into a two-year deal with The Associated Press for ChatGPT to ingest the publisher’s news stories. In December 2023, Open AI announced its first non-US partnership to train ChatGPT on German publisher Axel Springer’s content, including Business Insider. This was then followed by a similar deal in March 2024 with Le Monde and Prisa Media, news publishers from France and Spain. These partnerships are likely sought in an effort to avoid litigation like the case OpenAI and Microsoft are currently defending from the New York Times.

As it turns out, such deals are not limited to OpenAI or newsrooms. Book publishers have also gotten into the mix. Numerous reports recently pointed out that based on Taylor and Francis’s parent company’s market update, the British academic publishing giant has agreed to a $10 million USD AI training deal with Microsoft. Earlier this year, another major academic publisher, John Wiley and Sons, recorded $23 million in one-time revenue from a similar deal with a non-disclosed tech company. Meta even considered buying Simon & Schuster or paying $10 per book to acquire its rights portfolio for AI training.

With few exceptions (a notable one being Cambridge University Press), publishers have not bothered to ask their authors whether they approve of these agreements.

Does AI training require licensing to begin with?

First, it’s worth appreciating that these deals are made in the backdrop of some legal uncertainty. There are more than two dozen AI copyright lawsuits just in the United States, most of them turning on one key question: whether AI developers should have to obtain permission to scrap content to train AI models or whether fair use already allows this kind of training use even without permission.

The arguments for and against fair use for AI training data are well explained elsewhere. We think there are strong arguments, based on cases like Authors Guild v. Google, Authors Guild v. HathiTrust, and AV ex rel Vanderhye v. iParadigms, that the courts will conclude that copying to training AI models is fair use. We also think there are really good policy reasons to think this could be a good outcome if we want to encourage future AI development that isn’t dominated by only the biggest tech giants and that results in systems that produce less biased outputs. But we won’t know for sure whether fair use covers any and all AI training until some of these cases are resolved.

Even if you are firmly convinced that fair use protects this kind of use (and AI model developers have strong incentives to hold this belief), there are lots of other reasons why AI developers might seek licenses in order to navigate around the issue. This includes very practical reasons, like securing access to content in formats that make training easier, or content accompanied by structured, enhanced metadata. Given the pending litigation, licenses are also a good way to avoid costly litigation (copyright lawsuits are expensive, even if you win).

Although one can hardly blame these tech companies for making a smart business decision to avoid potential litigation, this could have a larger systematic impact on other players in the field, including the academic researchers who would like to rely on fair use to train AI. As IP scholar James Gibson explains, when risk-averse users create new licensing markets in gray areas of copyright law, copyright holders’ exclusive rights expands, and public interest diminishes. The less we rely on fair use, the weaker it becomes.

Finally, it’s worth noting that fair use is only available in the US and a few other jurisdictions. In other jurisdictions, such as within the EU, using copyrighted materials for AI training (especially for commercial purposes) may require a license.

To sum up: even though it may not be legally necessary to acquire copyright licenses for AI training, it seems that licensing deals between publishers and AI companies are highly likely to continue.

So, can publishers just do this without asking authors?

In a lot of cases, yes, publishers can license AI training rights without asking authors first. Many publishing contracts include a full and broad grant of rights–sometimes even a full transfer of copyright to the publisher for them to exploit those rights and to license the rights to third parties. For example, this typical academic publishing agreement provides that “The undersigned authors transfer all copyright ownership in and relating to the Work, in all forms and media, to the Proprietor in the event that the Work is published.” In such cases, when the publisher becomes the de facto copyright holder of a work, it’s difficult for authors to stake a copyright claim when their works are being sold to train AI.

Not all publishing contracts are so broad, however. For example, in the Model Publishing Contract for Digital Scholarship (which we have endorsed), the publisher’s sublicensing rights are limited and specifically defined, and profits resulting from any exploitation of a work must be shared with authors.

There are lots of variations, and specific terms matter. Some publisher agreements are far more limited–transferring only limited publishing and subsidiary rights. These limitations in the past have prompted litigation over whether the publisher or the author gets to control rights for new technological uses. Results have been highly dependent on the specific contract language used.

There are also instances where publishers aren’t even sure of what they own. For example, in the drawn-out copyright lawsuit brought by Cambridge University Press, Oxford University Press and Sage against Georgia State University, the court dropped 16 of the alleged 74 claimed instances of infringement because the publishers couldn’t produce documentation that they actually owned rights in the works they were suing over. This same lack of clarity contributed to the litigation and proposed settlement in the Google Books case, which is probably our closest analogy in terms of mass digitization and reuse of books (for a good discussion of these issues, see page 479 of this law review article by Pamela Samuelson about the Google Books settlement).

This is further complicated by the fact that authors sometimes are entitled to reclaim their rights, such as by rights reversion clause and copyright termination. Just because a publisher can produce the documentation of a copyright assignment, does not necessarily mean that the publisher is still the current copyright holder of a work.

We think it is certainly reasonable to be skeptical about the validity of blanket licensing schemes between large corporate rights holders and AI companies, at least when they are done at very large scale. Even though in some instances publishers do hold rights to license AI training, it is dubious whether they actually hold, and sufficiently document, all of the purported rights of all works being licensed for AI training.

Can authors at least insist on a cut of the profit?

It can feel pretty bad to discover that massively profitable publishers are raking in yet more money by selling licensing rights to your work, while you’re cut out of the picture. If they’re making money, why not the author?

It’s worth pointing out that, at least for academic authors, this isn’t exactly a novel situation–most academic authors make very little in royalties on their books, and nothing at all on their articles, while commercial publishers like Elsevier, Wiley, and SpringerNature sell subscription access at a healthy profit. Unless you have retained sublicensing rights, or your publishing contract has a profit-sharing clause, authors, unfortunately, are not likely to profit from the budding licensing market for AI training.

So what are authors to do?

We could probably start most posts like this with a big red banner that says “READ YOUR PUBLISHING CONTRACT!! (and negotiate it too)” Be on the lookout for what you are authorizing your publisher to do with your rights, and any language in it about reuse or the sublicensing of subsidiary rights.

You might also want to look for terms in your contract that speak to royalties and shares of licensing revenue. Some contracts have language that will allow you to demand an accounting of royalties; this may be an effective means of learning more about licensing deals associated with your work.

You can also take a closer look at clauses that allow you to revert rights–many contracts will include a clause under which authors can regain rights when their book falls below a certain sales threshold or otherwise becomes “out of print.” Even without such clauses, it is reasonable for authors to negotiate a reversion of rights when their books are no longer generating revenue. Our resources on reversion will give you a more in-depth look at this issue.

Finally, you can voice your support for fair use in the context of licensing copyrighted works for AI training. We think fair use is especially important to preserve for non-commercial uses. For example, academic uses could be substantially stifled if paid-for licensing for permission to engage in AI research or related uses becomes the norm. And in those cases, the windfall publishers hope to pocket isn’t coming from some tech giant, but ultimately is at the expense of researchers, their libraries and universities, and the public funding that goes to support them.

Introducing the Authors Alliance’s First Zine: Can Authors Address AI Bias?

Posted May 31, 2024

This guest post was jointly authored by Mariah Johnson and Marcus Liou, student attorneys in Georgetown’s Intellectual Property and Information Policy (iPIP) Clinic.

Generative AI (GenAI) systems perpetuate biases, and authors can have a potent role in mitigating such biases.

But GenAI is generating controversy among authors. Can authors do anything to ensure that these systems promote progress rather than prevent it? Authors Alliance believes the answer is yes, and we worked with them to launch a new zine, Putting the AI in Fair Use: Authors’ Abilities to Promote Progress, that demonstrates how authors can share their works broadly to shape better AI systems. Drawing together Authors Alliance’s past blog posts and advocacy discussing GenAI, copyright law, and authors, this zine emphasizes how authors can help prevent AI bias and protect “the widest possible access to information of all kinds.”

As former Copyright Register Barbara Ringer articulated, protecting that access requires striking a balance with “induc[cing] authors and artists to create and disseminate original works, and to reward them for their contributions to society.” The fair use doctrine is often invoked to do that work. Fair use is a multi-factor standard that allows limited use of copyrighted material—even without authors’ credit, consent, or compensation–that asks courts to examine:

(1) the purpose and character of the use,

(2) the nature of the copyrighted work,

(3) the amount or substantiality of the portion used, and

(4) the effect of the use on the potential market for or value of the work.

While courts have not decided whether using copyrighted works as training data for GenAI is fair use, past fair use decisions involving algorithms, such as Perfect 10, iParadigms, Google Books, and HathiTrust favored the consentless use of other people’s copyrighted works to create novel computational systems. In those cases, judges repeatedly found that algorithmic technologies aligned with the Constitutional justification for copyright law: promoting progress.

But some GenAI outputs prevent progress by projecting biases. GenAI outputs are biased in part because they use biased, low friction data (BLFD) as training data, like content scraped from the public internet. Examples of BLFD include Creative Commons (CC) licensed works, like Wikipedia, and works in the public domain. While Wikipedia is used as training data in most AI systems, its articles are overwhelmingly written by men–and that bias is reflected in shorter and fewer articles about women. And because the public domain cuts off in the mid-1920s, those works often reflect the harmful gender and racial biases of that time. However, if authors allow their copyrighted works to be used as GenAI training data, those authors can help mitigate some of the biases embedded in BLFD.

Current biases in GenAI are disturbing. As we discuss in our zine, word2vec is a very popular toolkit used to help machine learning (ML) models recognize relationships between words–like women as homemakers and Black men with the word “assaulted.” Similarly, OpenAI’s GenAI chatbox ChatGPT, when asked to generate letters of recommendation, used “expert,” “reputable,” and “authentic” to describe men and “beauty,” “stunning,” and “emotional” for women, discounting women’s competency and reinforcing harmful stereotypes about working women. An intersectional perspective can help authors see the compounding impact of these harms. What began as a legal framework to describe why discrimination law did not adequately address harms facing Black women, it is now used as a wider lens to consider how marginalization affects all people with multiple identities. Coined by Professor Kimberlé Crenshaw in the late 1980s, intersectionality uses critical theory like Critical Race Theory, feminism, and working-class studies together as “a lens . . . for seeing the way in which various forms of inequality often operate together and exacerbate each other.” Contemporary authors’ copyrighted works often reflect the richness of intersectional perspectives, and using those works as training data can help mitigate GenAI bias against marginalized people by introducing diverse narratives and inclusive language. Not always–even recent works reflect bias–but more often than might be possible currently.

Which brings us back to fair use. Some corporations may rely on the doctrine to include more works by or about marginalized people in an attempt to mitigate GenAI bias. Professor Mark Lemley and Bryan Casey have suggested “[t]he solution [to facial recognition bias] is to build bigger databases overall or to ‘oversample’ members of smaller groups” because “simply restricting access to more data is not a viable solution.” Similarly, Professor Matthew Sag notes that “[r]estricting the training data for LLMs to public domain and open license material would tend to encode the perspectives, interests, and biases of a distinctly unrepresentative set of authors.” However, many marginalized people may wish to be excluded from these databases rather than have their works or stories become grist for the mill. As Dr. Anna Lauren Hoffman warns, “[I]nclusion reinforces the structural sources of violence it supposedly addresses.”

Legally, if not ethically, fair use may moot the point. The doctrine is flexible, fact-dependent, and fraught. It’s also fairly predictable, which is why legal precedent and empirical work have led many legal scholars to believe that using copyrighted works as training data to debias AI will be fair use–even if that has some public harms. Back in 2017, Professor Ben Sobel concluded that “[i]f engineers made unauthorized use of copyrighted data for the sole purpose of debiasing an expressive program, . . . fair use would excuse it.” Professor Amanda Levendowski has explained why and how “[f]air use can, quite literally, promote creation of fairer AI systems.” More recently, Dr. Mehtab Khan and Dr. Alex Hanna observed that “[a]ccessing copyright work may also be necessary for the purpose of auditing, testing, and mitigating bias in datasets . . . [and] it may be useful to rely on the flexibility of fair use, and support access for researchers and auditors.”

No matter how you feel about it, fair use is not the end of the story. It is ill-equipped to solve the troubling growth of AI-powered deepfakes. After being targeted by sexualized deepfakes, Rep. Ocasio-Cortez described “[d]eepfakes [as] absolutely a way of digitizing violent humiliation against other people.” Fair use will not solve the intersectional harms of AI-powered face surveillance either. Dr. Joy Buolamwini and Dr. Timnit Gebru evaluated leading gender classifiers used to train face surveillance technologies and discovered that they more accurately classified males over females and lighter-skinned over darker-skinned people. The researchers also discovered that the “classifiers performed worst on darker female subjects.” While legal scholars like Professors Shyamkrishna Balganesh, Margaret Chon, and Cathay Smith argue that copyright law can protect privacy interests, like the ones threatened by deepfakes or face surveillance, federal privacy laws are a more permanent, comprehensive way to address these problems.

But who has time to wait on courts and Congress? Right now, authors can take proactive steps to ensure that their works promote progress rather than prevent it. Check out the Authors Alliance’s guides to Contract Negotiations, Open Access, Rights Reversion, and Termination of Transfer to learn how–or explore our new zine, Putting the AI in Fair Use: Authors’ Abilities to Promote Progress.

You can find a PDF of the Zine here, as well as printer-ready copies here and here.

Books are Big AI’s Achilles Heel

Posted May 13, 2024

By Dave Hansen and Dan Cohen

Image of the Rijksmuseum by Michael D Beckwith. Image dedicated to the Public Domain.

Rapidly advancing artificial intelligence is remaking how we work and live, a revolution that will affect us all. While AI’s impact continues to expand, the operation and benefits of the technology are increasingly concentrated in a small number of gigantic corporations, including OpenAI, Google, Meta, Amazon, and Microsoft.

Challenging this emerging AI oligopoly seems daunting. The latest AI models now cost billions of dollars, beyond the budgets of startups and even elite research universities, which have often generated the new ideas and innovations that advance the state of the art.

But universities have a secret weapon that might level the AI playing field: their libraries. Computing power may be one important part of AI, but the other key ingredient is training data. Immense scale is essential for this data—but so is its quality.

Given their voracious appetite for text to feed their large language models, leading AI companies have taken all the words they can find, including from online forums, YouTube subtitles, and Google Docs. This is not exactly “the best that has been thought and said,” to use Matthew Arnold’s pointed phrase. In Big AI’s haphazard quest for quantity, quality has taken a back seat. The frequency of “hallucinations”—inaccuracies currently endemic to AI outputs—are cause for even greater concern.

The obvious way to rectify this lack of quality and tenuous relationship to the truth is by ingesting books. Since the advent of the printing press, authors have published well over 100 million books. These volumes, preserved for generations on the shelves of libraries, are perhaps the most sophisticated reflection of human thinking from the beginning of recorded history, holding within them some of our greatest (and worst) ideas. On average, they have exceptional editorial quality compared to other texts, capture a breadth and diversity of content, a vivid mix of styles, and use long-form narrative to communicate nuanced arguments and concepts.

The major AI vendors have sought to tap into this wellspring of human intelligence to power the artificial, although often through questionable methods. Some companies have turned to an infamous set of thousands of books, apparently retrieved from pirate websites without permission, called “Books3.” They have also sought licenses directly from publishers, using their massive budgets to buy what they cannot scavenge. Meta even considered purchasing one of the largest publishers in the world, Simon & Schuster.

As the bedrock of our shared culture, and as the possible foundation for better artificial intelligence, books are too important to flow through these compromised or expensive channels. What if there were a library-managed collection made available to a wide array of AI researchers, including at colleges and universities, nonprofit research institutions, and small companies as well as large ones?

Such vast collections of digitized books exist right now. Google, by pouring millions of dollars into its long-running book scanning project, has access to over 40 million books, a valuable asset they undoubtedly would like to keep exclusive. Fortunately, those digitized books are also held by Google’s partner libraries. Research libraries and other nonprofits have additional stockpiles of digitized books from their own scanning operations, derived from books in their own collections. Together, they represent a formidable aggregation of texts.

A library-led training data set of books would diversify and strengthen the development of AI. Digitized research libraries are more than large enough, and of substantially higher quality, to offer a compelling alternative to existing scattershot data sets. These institutions and initiatives have already worked through many of the most challenging copyright issues, at least for how fair use applies to nonprofit research uses such as computational analysis. Whether fair use also applies to commercial AI, or models built from iffy sources like Books3, remains to be seen.

Library-held digital texts come from lawfully acquired books—an investment of billions of dollars, it should be noted, just like those big data centers—and libraries are innately respectful of the interests of authors and rightsholders by accounting for concerns about consent, credit, and compensation. Furthermore, they have a public-interest disposition that can take into account the particular social and ethical challenges of AI development. A library consortium could distinguish between the different needs and responsibilities of academic researchers, small market entrants, and large commercial actors.

If we don’t look to libraries to guide the training of AI on the profound content of books, we will see a reinforcement of the same oligopolies that rule today’s tech sector. Only the largest, most well-resourced companies will acquire these valuable texts, driving further concentration in the industry. Others will be prevented from creating imaginative new forms of AI based on the best that has been thought and said. As they have always done, by democratizing access libraries can support learning and research for all, ensuring that AI becomes the product of the many rather than the few.

Further reading on this topic: “Towards a Books Data Commons for AI Training,” by Paul Keller, Betsy Masiello, Derek Slater, and Alek Tarkowski.

This week, Authors Alliance celebrates its 10th anniversary with an event in San Francisco on May 17 (We still have space! Register for free here) titled “Authorship in an Age of Monopoly and Moral Panics,” where we will highlight obstacles and opportunities of new technology. This piece is part of a series leading up to the event.

Writing About Real People Update: Right of Publicity, Voice Protection, and Artificial Intelligence

Posted March 7, 2024

Some of you may recall that Authors Alliance published our long-awaited guide, Writing About Real People, earlier this year. One of the major topics in the guide is the right of publicity—a right to control use of one’s own identity, particularly in the context of commercial advertising. These issues have been in the news a lot lately as generative AI poses new questions about the scope and application of the right of publicity.

Sound-alikes and the Right of Publicity

One important right of publicity question in the genAI era concerns the increasing prevalence of “sound-alikes” created using generative AI systems. The issue of AI-generated voices that mimicked real people came to the public’s attention with the apparently convincing “Heart on My Sleeve” song, imitating Drake and the Weeknd, and tools that facilitate creating songs imitating popular singers have increased in number and availability.

AI-generated soundalikes are a particularly interesting use of this technology when it comes to the right of publicity because one of the seminal right of publicity cases, taught in law schools and mentioned in primers on the topic, concerns a sound-alike from the analog world. In 1986, the Ford Motor Company hired an advertising agency to create a TV commercial. The agency obtained permission to use “Do You Wanna Dance,” a song Bette Midler had famously covered, in its commercial. But when the ad agency approached Midler about actually singing the song for the commercial, she refused. The agency then hired a former backup singer of Midler’s to record the song, apparently asking the singer to imitate Midler’s voice in the recording. A federal court found that this violated Midler’s right of publicity under California law, even though her voice was not actually used. Extending this holding to AI-generated voices seems logical and straightforward—it is not about the precise technology used to create or record the voice, but about the end result the technology is used to achieve.

Right of Publicity Legislation

The right of publicity is a matter of state law. In some states, like California and New York, the right of publicity is established via statute, and in others, it’s a matter of common law (or judge-made law). In recent months, state legislatures have proposed new laws that would codify or expand the right of publicity. Similarly, many have called for the establishment of a federal right of publicity, specifically in the context of harms caused by the rise of generative AI. One driving force behind calls for the establishment of a federal right of publicity is the patchwork nature of state right of publicity laws: in some states, the right of publicity extends only to someone’s name, image, likeness, voice, and signature, but in others, it’s much broader. While AI-generated content and the ways in which it is being used certainly pose new challenges for courts considering right of publicity violations, we are skeptical that new legislation is the best solution.

In late January, the No Artificial Intelligence Fake Replicas and Unauthorized Duplications Act of 2024 (or “No AI FRAUD Act”) was introduced in the House of Representatives. The No AI FRAUD Act would create a property-like right in one’s voice and likeness, which is transferable to other parties. It targets voice “cloning services” and mentions the “Heart on My Sleeve” controversy specifically. But civil societies and advocates for free expression have raised alarm about the ways in which the bill would make it easier for creators to actually lose control over their own personality rights while also impinging on others’ First Amendment rights due to its overbreadth and the property-like nature of the right it creates. While the No AI FRAUD Act contains language stating that the First Amendment is a defense to liability, it’s unclear how effective this would be in practice (and as we explain in the Writing About Real People Guide, the First Amendment is always a limitation on laws affecting freedom of expression).

The Right of Publicity and AI-Generated Content

In the past, the right of publicity has been described as “name, image, and likeness” rights. What is interesting about AI-generated content and the right of publicity is that a person’s likeness can be used in a more complete way than ever before. In some cases, both their appearance and voice are imitated, associated with their name, and combined in a way that makes the imitation more convincing.

What is different about this iteration of right of publicity questions is the actors behind the production of the soundalikes and imitations, and, to a lesser extent, the harms that might flow from these uses. A recent use of a different celebrity’s likeness in connection with an advertisement is instructive on this point. Earlier this year, advertisements emerged on various platforms featuring an AI-generated Taylor Swift participating in a Le Creuset cookware giveaway. These ads contained two separate layers of deceptiveness: most obviously, that Swift was AI-generated and did not personally appear in the ad, but more bafflingly, that they were not Le Creuset ads at all. The ads were part of a scam whereby users might pay for cookware they would never receive, or enter credit card details which could then be stolen or otherwise used for improper purposes. Compared to more traditional conceptions of advertising, the unfair advantages and harms caused by the use of Swift’s voice and likeness are much more difficult to trace. Taylor Swift’s likeness and voice were appropriated by scammers to trick the public into thinking they were interacting with Le Creuset advertising.

It may be that the right of publicity as we know it (and as we discuss it in the Writing About Real People Guide) is not well-equipped to deal with these kinds of situations. But it seems to us that codifying the right of publicity in federal law is not the best approach. Just as Bette Midler had a viable claim under California’s right of publicity statute back in 1992, Taylor Swift would likely have a viable claim against Le Creuset if her likeness had been used by that company in connection with commercial advertising. The problem is not the “patchwork of state laws,” but that this kind of doubly-deceptive advertising is not commercial advertising at all. On a practical level, it’s unclear what party could even be sued by this kind of use. Certainly not Le Creuset. And it seems to us unfair to say that the creator of the AI technology sued should be left holding the bag, just because someone used it for fraudulent purposes. The real fraudsters—anonymous but likely not impossible to track down—are the ones who can and should be pursued under existing fraud laws.

Authors Alliance has said elsewhere that reforms to copyright law cannot be the solution to any and all harms caused by generative AI. The same goes for the intellectual property-like right of publicity. Sensible regulation of platforms, stronger consumer protection laws, and better means of detecting and exposing AI-generated content are possible solutions to the problems that the use of AI-generated celebrity likenesses have brought about. To instead expand intellectual property rights under a federal right of publicity statute risks infringing on our First Amendment freedoms of speech and expression.

Federal Right of Publicity Takes Center Stage in Senate Hearing on AI

Posted July 28, 2023

The Authors Alliance found this write-up by Professor Jennifer Rothman at the University of Pennsylvania useful and wanted to share it with our readers. You can find Professor Rothman’s original post on her website, Rothman’s Roadmap to the Right of Publicity, here.

By Jennifer Rothman

On July 12th, the Senate Judiciary Committee’s Subcommittee on Intellectual Property held its second hearing about artificial intelligence (AI) and intellectual property, this one was to focus expressly on “copyright” law. Although copyright was mentioned many times during the almost two-hour session and written testimony considered whether the use of unlicensed training data was copyright infringement, a surprising amount of the hearing focused not on copyright law, but instead on the right of publicity.

Both senators and witnesses spent significant time advocating for new legislation—a federal right of publicity or a federal anti-impersonation right (what one witness dubbed the FAIR Act). Discussion of such a federal law occupied more of the hearing than predicted and significantly more time than was spent parsing either existing copyright law or suggesting changes to copyright law.

In Senator Christopher Coons’s opening remarks, he suggested that a federal right of publicity should be considered to address the threat of AI to performers. At the start of his comments, Coons played an AI-generated song about AI set to the tune of New York, New York in the vocal style of Frank Sinatra. Notably, Coons highlighted that he had sought and received permission to use both the underlying copyrighted material and Frank Sinatra’s voice.

In addition to Senator Coons, Senators Marsha Blackburn and Amy Klobuchar expressly called for adding a federal right of publicity. Blackburn, a senator from Tennessee, highlighted the importance of name and likeness rights for the recording artists, songwriters, and actors in her state and pointed to the concerns raised by the viral AI-generated song “Heart on My Sleeve”. This song was created by a prompt to produce a song simulating a song created by and sung by Drake and The Weekend. Ultimately, Universal Music Group got platforms, such as Apple Music and Spotify, to take the song down on the basis of copyright infringement claims. Universal alleged that the use infringed Drake and The Weekend’s copyrighted music and sound recordings. The creation (and popularity!) of the song sent shivers through the music industry.

It therefore is no surprise that Jeffrey Harleston, General Counsel for Universal Music Group, advocated both in his oral and written testimony for a federal right of publicity to protect against “confusion, unfair competition[,] market dilution, and damage” to the reputation and career of recording artists if their voices or vocal styles are imitated in generative AI outputs. Karla Ortiz, a concept artist and illustrator, known for her work on Marvel films, also called for a federal right of publicity in her testimony. Her concerns were tied to the use of her name as a prompt to produce outputs trained on her art in her style and that could substitute for hiring her to create new works. Law Professor Matthew Sag supported adoption of a federal right of publicity to address the “hodgepodge” of state laws in the area.

Dana Rao, the Executive Vice President and General Counsel of Adobe, expressed support for a federal anti-impersonation right, which he noted had a catchy acronym—the FAIR Act. His written testimony on behalf of Adobe highlighted its support for such a law and gave the most details of what such a right might look like. Adobe suggested that such an anti-impersonation law would “offer[] artists protection against” direct economic competition of an AI-generated replication of their style and suggested that this law “would provide a right of action to an artist against those that are intentionally and commercially impersonating their work through AI tools. This type of protection would provide a new mechanism for artists to protect their livelihood from people misusing this new technology, without having to rely solely on copyright, and should include statutory damages to alleviate the burden on artists to prove actual damages, directly addressing the unfairness of an artist’s work being used to train an AI model that then generates outputs that displace the original artist.” Adobe was also open to adoption of “a federal right of publicity . . . to help address concerns about AI being used without permission to copy likenesses for commercial benefit.”

Although some of the testimony supporting a federal right of publicity suggested that many states already extend such protection, there was a consensus that a preemptive federal right could provide greater predictability, consistency, and protection. Senator Klobuchar and Universal Music’s Harleston emphasized the value of elevating the right of publicity to a federal “intellectual property” right. Notably, this would have the bonus of clarifying an open question of whether right of publicity claims are exempted from the Communications Decency Act’s § 230 immunity provision for third-party speech conveyed over internet platforms. (See, e.g. Hepp v. Facebook.)

Importantly, Klobuchar noted the overlap between concerns over commercial impersonation and concerns over deepfakes that are used to impersonate politicians and create false and misleading videos and images that pose a grave danger to democracy.

Of course, the proof is in the pudding. No specific legislation has been floated to my knowledge and so I cannot evaluate its effectiveness or pitfalls. Although the senators and witnesses who spoke about the right of publicity were generally supportive, the details of what such a law might look like were vague.

From the right-holders’ (or identity-holders’) perspective the scope of such a right is crucial. Many open questions exist. If preemptive in nature, how would such a statute affect longstanding state law precedents and the appropriation branch of the common law privacy tort that in many states is the primary vehicle for enforcing the right of publicity? When confronted with similar concerns over adopting a new “right of publicity” to replace New York’s longstanding right of privacy statute that protected against the misappropriation of a person’s name, likeness, and voice, New York legislators astutely recognized the danger of unsettling more than 100 years of precedents that had provided (mostly) predictable protection for individuals in the state.

Another key concern is whether these rights will be transferable away from the underlying identity-holders. If they are, then a federal right of publicity will have a limited and potentially negative impact on the very people who are supposedly the central concern driving the proposed law. This very concern is central to the demands of SAG-AFTRA as part of its current strike. The actors’ union wants to limit the ability of studios and producers to record a person’s performance in one context and then use AI and visual effects to create new performances in different contexts. As I have written at length elsewhere, a right of publicity law (whether federal or otherwise) that does not limit transferability will make identity-holders more vulnerable to exploitation rather than protect them. (See, e.g., Jennifer E. Rothman, The Inalienable Right of Publicity, 100 Georgetown L.J. 185 (2012); Jennifer E. Rothman, What Happened to Brooke Shields was Awful. It Could Have Been Worse, Slate, April 2023.)

Professor Matthew Sag rightly noted the importance of allowing ordinary people—not just the famous or commercially successful—to bring claims for publicity violations. This is a position with which I wholeheartedly agree, but Sag, when pressed on remedies, suggested that there should not be statutory damages. Yet, such damages are usually the best and sometimes only way for ordinary individuals to be able to recover damages and to get legal assistance to bring such claims. In fact, what is often billed as California’s statutory right of publicity for the living (Cal. Civ. Code § 3344) was originally passed under the moniker “right of privacy” and was specifically adopted to extend statutory damages to plaintiffs who did not have external commercial value making damage recovery challenging. (See Jennifer E. Rothman, The Right of Publicity: Privacy Reimagined for a Public World (Harvard Univ. Press 2018)). Notably, Dana Rao of Adobe, recognizing this concern, specifically advocated for the adoption of statutory damages.

The free speech and First Amendment concerns raised by the creation of a federal right of publicity will turn on the specific scope and likely exceptions to such a law. Depending on the particulars, it may be that potential defendants stand more to gain by a preemptive federal law than potential plaintiffs do. If there are clear and preemptive exemptions to liability this will be a win for many repeat defendants in right of publicity cases who now have to navigate a wide variety of differing state laws. And if liability is limited to instances in which there is likely confusion as to participation or sponsorship, the right of publicity will be narrowed from its current scope in most states. (See Robert C. Post and Jennifer E. Rothman, The First Amendment and the Right(s) of Publicity, 130 Yale L.J. 86 (2020)).

In short, the focus in this hearing on “AI and Copyright” on the right of publicity instead supports my earlier take that the right of publicity may pose a significant legal roadblock for developers of AI. Separate from legal liability, AI developers should take seriously the ethical concerns of producing outputs that imitate real people in ways that confuse as to their participation in vocal or audiovisual performances, or in photographs.