Earlier today Authors Alliance joined a broad coalition of public interest organizations, creators, academics, and others in a letter to members of Congress urging caution when considering proposals to revamp copyright law to address concerns about artificial intelligence. As we explained previously, we believe that copyright law currently has the appropriate tools needed to both protect creators and encourage innovation.
As the letter states, the signatories share a common interest in ensuring that artificial intelligence meets its potential to enrich the American economy, empower creatives, accelerate the progress of science and useful arts, and expand humanity’s overall welfare. Many creators are already using AI to conduct innovative new research, address long-standing questions, and produce new creative works. Some, such as some of these artists, have used this technology for many years.
So our message is simple: existing copyright doctrine has evolved and adapted to accommodate many revolutionary technologies, and is well equipped to address the legitimate concerns of creators. Our courts are the proper forum to apply those doctrines to the myriad fact patterns that AI will present over the coming years and decades.
Current copyright law isn’t perfect, and we certainly believe creativity and innovation would benefit from some changes. However, we should be careful about reactionary, alarmist politics. It seldom makes for good law. Unfortunately, that’s what we’re seeing right now with AI, and we hope that Congress has the wisdom to see through it.
We encourage our members to reach out to your own Congressional representative to express the need to tread carefully, and (if you are) to explain how you are using AI in your work. We’d also be very happy to hear from you as we develop our own further policy communications to Congress and to agencies such as the U.S. Copyright Office.
Authors Alliance is pleased to announce that in recent weeks, we have submitted petitions to the Copyright Office requesting that it recommend renewing expanding the existing text data mining exemptions to DMCA liability to make the current legal carve-out that enables text and data mining more flexible, so that researchers can share their corpora of works with other researchers who want to conduct their own text data mining research. On each of these petitions, we were joined by two co-petitioners, the American Association of University Professors and the Library Copyright Alliance. These were short filings—requesting changes and providing brief explanations—and will be the first of many in our efforts to obtain a renewal and expansion of the existing TDM exemptions.
Background
The Digital Millennium Copyright Act (DMCA) includes a provision that forbids people from bypassing technical protection measures on copyrighted works. But it also implements a triennial rulemaking process whereby organizations and individuals can petition for temporary exemptions to this rule. The Office recommends an exemption when its proponents show that they, or those they represent, are “adversely affected in their ability to make noninfringing [fair] uses due to the prohibition on circumventing access controls.” Every three years, petitioners must ask the Office to renew existing exemptions in order for them to continue to apply. Petitioners can also ask the Office to recommend expanding an existing exemption, which requires the same filings and procedure as petitioning for a new exemption.
Back in 2020, during the eighth of these triennial rulemakings, Authors Alliance—along with the Library Copyright Alliance and the American Association of University Professors—petitioned the Copyright Office to create an exemption to DMCA liability that would enable researchers to conduct text and data mining. Text and data mining is a fair use, and the DMCA prohibitions on bypassing DRM and similar technical protection measures made it difficult or even impossible for researchers to conduct text and data mining on in-copyright e-books and films. After a long process which included filing a comment in support of the exemption and an ex parte meeting with the Copyright Office, the Office ultimately recommended that the Librarian of Congress grant our proposed exemption (which she did). The Office also recommended that the exemption be split into two parts, with one exemption addressing literary works distributed electronically, and the other addressing films.
While the ninth triennial rulemaking does not technically happen until 2024, petitions for renewals, expansions, and new exemptions have already been filed.
Our Petitions
Back in early July, we made our first filings with the Copyright Office in the form of renewal petitions for both exemptions. For this step, proponents of current exemptions simply ask the Copyright Office to renew them for another three year cycle, accompanied by a short explanation of whether and how the exemption is being used and a statement that neither law nor technology has changed such that the exemption is no longer warranted. Other parties are then given an opportunity to respond to or oppose renewal petitions. The Office recommends that exemption proponents who want to expand a current exemption also petition for its renewal—which is just what we did. In our renewal petitions, we explained how researchers are using the exemptions and how neither recent case law nor the continued availability of licensed TDM databases represent changes in the law or technology, making renewal of the TDM exemptions proper and justified. The renewal petitions follow a streamlined process, where they are generally simply granted unless the Office finds there to be “meaningful opposition” to a renewal petition, articulating a change in the law or facts. You can find our renewal petition for the literary works TDM exemption here, and our renewal petition for the film TDM exemption here.
But we also sought to expand the current exemptions, in two petitions submitted a few weeks back. In our expansion petitions, we proposed a simple change that we would like to see made to the current DMCA exemptions for text data mining. In the exemption’s current form, academic researchers can bypass technical protection measures to assemble a corpus on which to conduct TDM research, but they can only share it with other researchers for purposes of “collaboration and verification.” We asked the Office to permit these researchers to share their corpora with other researchers who want to use the corpus to conduct TDM research, but are not direct collaborators. However, this second group of researchers would still have to comply with the various requirements of the exemption, such as complying with security measures. Essentially, we seek to expand the sharing provision of the current exemption while leaving the other provisions intact. This is largely based on feedback we have received from those using the exemption and our understanding of how the regulation can be improved so that their desired noninfringing uses are no longer adversely affected by this limitation. You can find our expansion petition for the literary works TDM exemption here, and our expansion petition for the film TDM exemption here.
What’s Next?
The next step in the triennial rulemaking process is the Copyright Office issuing a notice of proposed rulemaking, where it will lay out its plan of action. While we do not have a set timeline for the notice of proposed rulemaking, during the last rulemaking cycle, it happened in mid-October—meaning it is reasonable to expect the Office to issue this notice in the next two months or so. Then, there will be several rounds of comments in support of or in opposition to the proposals. Finally, the Office will issue a final recommendation, and the Librarian of Congress will issue a final rule. While the Librarian of Congress is not legally obligated to adopt the Copyright Office’s recommendations, they traditionally do. Based on last year’s cycle, we can expect a final rule to be issued around October 2024. So we are in for a long wait and a lot of work! We will keep our readers updated as the rulemaking moves forward.
Authors Alliance readers will surely have noticed that we have been writing a lot about generative AI and copyright lately. Since the Copyright Office issued its decision letter on copyright registration in a graphic novel that included AI-generated images a few months back, many in the copyright community and beyond have struggled with the open questions around generative AI and copyright.
The Copyright Office has launched an initiative to study generative AI and copyright, and today issued a notice of inquiry to solicit input on the issues involved. The Senate Judiciary Committee has also held multiple hearings on IP rights in AI-generated works, including one last month focused on copyright. And of course there are numerous lawsuits pending over its legality, based on theories ranging from copyright infringement to to privacy to defamation. It’s also clear that there is little agreement about a one-size-fits-all rule for AI-generated works that applies across industries.
At Authors Alliance, we care deeply about access to knowledge because it supports free inquiry and learning, and we are enthusiastic about ways that generative AI can meaningfully further those ideals. In addition to all the mundane but important efficiency gains generative AI can assist with, we’ve already seen authors incorporate generative AI into their creative processes to produce new works. We’ve also seen researchers incorporate these tools to help make new discoveries. There are some clear concerns about how generative AI tools, for example, can make it easier to engage in fraud and deception, as well as perpetuating disinformation. There have been many calls for legal regulation of generative AI technologies in recent months, and we wanted to share our views on the copyright questions generative AI poses, recognizing that this is a still-evolving set of questions.
Copyright and AI
Copyright is at its core an economic regulation meant to provide incentives for creators to produce and disseminate new expressive works. Ultimately, its goal is to benefit the public by promoting the “progress of science,” as the U.S. Constitution puts it. Because of this, we think new technology should typically be judged by what it accomplishes with respect to those goals, and not by the incidental mechanical or technological means that it uses to achieve its ends.
Within that context, we see generative AI as raising three separate and distinct legal questions. The first and perhaps most contentious is whether fair use should permit use of copyrighted works as training data for generative AI models. The second is how to treat generative AI outputs that are substantially similar to existing copyrighted works used as inputs for training data—in other words, how to navigate claims that generative AI outputs infringe copyright in existing works. The third question is whether copyright protection should apply to new outputs created by generative AI systems. It is important to consider these questions separately, and avoid the temptation to collapse them into a single inquiry, as different copyright principles are involved. In our view, existing law and precedent give us good answers to all three questions, though we know those answers may be unpalatable to different segments of a variety of content industries.
Training Data and Fair Use
The first area of difficulty concerns the inputstage of generative AI. Is the use of training data which includes copyrighted works a fair use, or does it infringe on a copyright owner’s exclusive rights in her work? The generative AI models used by companies like OpenAI, Stability AI, and Stable Diffusion are based on massive sets of training data. Much of the controversy around intellectual property and generative AI concerns the fact that these companies often do not seek permission from rights holders before training their models on works controlled by these rights holders (although some companies, like Adobe, are building generative AI models based on their own stock images, openly-licensed images, and public domain content). Furthermore, due to the size of the data sets and nature of their collection (often obtained via scraping websites), the companies that deploy these models do not make clear what works make up the training data. This question is one that is controversial and highly debated in the context of written works, images, and songs. Some creators and creator communities in these areas have made calls for “consent, credit, and compensation” when their works are included in training data. The obstacle to that point of view is, if the use of training data is a fair use, none of this is required, at least not by copyright.
We believe that the use of copyrighted works as training data for generative AI tools should generally be considered fair use. We base this view on our reading of numerous fair use precedents including Google Books and HathiTrust cases as well others such as iParadigms.These and other cases support the idea that fair use allows for copying for non-expressive uses—copying done as an “intermediate step” in producing non-infringing content, such as by extracting non-expressive content such as patterns, facts, and data in or about the work. The notion that non-expressive (also called “non-consumptive”) uses do not infringe copyrights is based in large part on a foundational principle in copyright law: copyright protection does not extend to facts or ideas. If it did, copyright law would run the risk of limiting free expression and inhibiting the progress of knowledge rather than furthering it. Using in-copyright works to create a tool or model with a new and different purpose from the works themselves, which does not compete with those works in any meaningful way, is a prototypical fair use. Like the Google Books project (as well as text data mining), generative AI models use data (like copyrighted works) to produce information about the works they ingest, including abstractions and metadata, rather than replicating expressive text.
In addition, fair use of copyrighted works as training data for generative AI has several practical implications for the public utility of these tools. For example, without it, AI could be trained on only “safe materials,” like public domain works or materials specifically authorized for such use. Models already contain certain filters—often excluding hateful content or pornography as part of its training set. However, a more general limit on copyrighted content—virtually all creative content published in the last one hundred years—would tend to amplify bias and the views of an unrepresentative set of creators.
Generative AI Outputs and Copyright Infringement
The feature that most distinguishes generative AI from technology in copyright cases that preceded it, such as Google Books and HathiTrust, is that generative AI not only ingests copyrighted works for the purpose of extracting data for analysis or search functionality, but for using this extracted data to produce new content. Can content produced by a generative AI tool infringe on existing copyrights?
Some have argued that the use of training data in this context is not a fair use, and is not truly a “non-expressive use” because generative AI tools produce new works based on data from originals and because these new works could in theory serve as market competitors for works they are trained on. While it is a fair point that generative AI is markedly different from those earlier technologies because of these outputs, the point also conflates the question of inputs and outputs. In our view, e using copyrighted works as inputs to develop a generative AI tool is generally not infringement, but this does not mean that the tool’s outputs can’t infringe existing copyrights.
We believe that while inputs as training data is largely justifiable as fair use, it is entirely possible that certain outputs may cross the line into infringement. In some cases, a generative AI tool can fall into the trap of memorizing inputs such that it produces outputs that are essentially identical to a given input. While evidence to date indicates that memorization is rare, it does exist.
So how should copyright law address outputs that are essentially memorized copies of inputs? We think the law already has the tools it needs to address this. Where fair use does not apply, copyright’s “substantial similarity” doctrine is equipped to handle the question of whether a given output is similar enough to an input to be infringing. The substantial similarity doctrine is appropriately focused on protection of creative expression while also providing room for creative new uses that draw on unprotectable facts or ideas. Substantial similarity is nothing new: it has been a part of copyright infringement analysis for decades, and is used by federal courts across the country. And it may well be that standards, such as a set of “Best Practices for Copyright Safety for Generative AI” proposed by law professor Matthew Sag, will become an important measure of assessing whether companies offering generative AI have done enough to guard against the risk of their tools producing infringing outputs.
Copyright Protection of AI Outputs
A third major question is, what exactly is the copyright status of the outputs of generative AI programs: are they protected by copyright at all, and if so, who owns those copyrights? Under the Copyright Office’s recent registration guidance, the answer seems to be that there is no copyright protection in the outputs. This does not sit well with some generative AI companies or many creators who rely on generative AI programs in their own creative work.
We generally agree with the Copyright Office’s recent guidance concerning the copyright status of AI-generated works, and believe that they are unprotected by copyright. This is based on the simple but enduring “human authorship” requirement in copyright law, which dates back to the late 19th century. In order to be protected by copyright, a work must be the product of a human author and contain a modicum of human creativity. Purely mechanical processes that occur without meaningful human creative input cannot generate copyrightable works. The Office has categorized generative AI models as this kind of mechanical tool: the output responds to the human prompt, but the human making the prompt does not have sufficient control over how the model works to make them an “author” of the output for the purposes of copyright law. The district court for D.C. recently issued a decision agreeing with this take in Thaler v. Perlmutter, a case that challenged the human authorship requirement in the context of generative AI.
It’s interesting to note here that in the Copyright Office listening session on text-based works, participants nearly universally agreed that outputs should not be protected by copyright, agreeing with the Copyright Office’s guidance. Yet the other listening sessions had more of a diversity of views. In particular, the participants in the listening sessions on audiovisual works and sound recordings were concerned about this issue. In industries like the music and film industries, where earlier iterations of generative AI tools have long been popular (or are even industry norms), the prospect of being denied copyright protection in songs or films, simply due to the tools used, can understandably be terrifying for creators who want to make a profit from their works. On this front, we’re sympathetic. Creators who rely on their copyrights to defend and monetize their works should be permitted to use generative AI as a creative tool without losing that protection. While we believe that the human authorship requirement is sound, it would be helpful to have more clarity on the status of works that incorporate generative AI content. How much additional human creativity is needed to render an AI-generated work a work of human authorship, and how much can a creator use a generative AI tool as part of their creative process without foregoing copyright protection in the work they produce? The Copyright Office seems to be grappling with these questions as well and seeking to provide additional guidance, such as in a recent webinar with more in-depth registration guidance for creators relying on generative AI tools in their creative efforts.
Other Information Policy Issues Affecting Authors
Generative AI has generated questions in other areas of information policy beyond the copyright questions we discuss above. Fraudulent content or disinformation, the harm caused by deep fakes and soundalikes, defamation, and privacy violations are serious problems that ought to be addressed. Those uses do nothing to further learning, and actually pollute public discourse rather than enhance it. They can also cause real monetary and reputational harm to authors.
In some cases, these issues can be addressed by information policy doctrines outside of copyright, and in others, they can be best handled by regulations or technical standards addressing development and use of generative AI models. A sound application of state laws such as defamation law, right of publicity laws, and various privacy torts could go a long way towards mitigating these harms. Some have proposed that the U.S. implement new legislation to enact a federal right of publicity. This would represent a major change in law and the details of such a proposal would be important. Right now, we are not convinced that this would serve creators better than the existing state laws governing the right of publicity. While it may take some time for courts to figure out how to adapt legal regimes outside of copyright to questions around generative AI, adapting the law to new technologies is nothing new. Other proposals call for regulations like labeling AI-generated content, which could also be reasonable as a tool to combat disinformation and fraudulent content.
In other cases, creators’ interests could be protected through direct regulation of the development and use of generative AI models. For example, certain creators’ desire for consent, credit, and compensation when their works are included in training data sets for generative AI programs is an issue that could be perhaps addressed through regulation of AI models. As for consent, some have called for an opt-out system where creators could have their works removed from the training data, or the deployment of a “do not train” tag similar to the robots.txt “do not crawl” tag. As we explain above, under the view that training data is generally a fair use, this is not required by copyright law. But the views that using copyrighted training data without some sort of recognition of the original creator is unfair, which many hold, may support arguments for other regulatory or technical approaches that would encourage attribution and pathways for distributing new revenue streams to creators.
Similarly, some have called for collective licensing legislation for copyrighted content used to train generative AI models, potentially as an amendment to the Copyright Act itself. We believe that this would not serve the creators it is designed to protect and we strongly oppose it. In addition to conflicting with the fundamental principles of fair use and copyright policy that have made the U.S. a leader in innovation and creativity, collective licensing at this scale would be logistically infeasible and ripe for abuse, and would tend to enrich established, mostly large rights holders while leaving out newer entrants. Similar efforts several years ago were proposed and rejected in the context of mass digitization based on similar concerns.
Generative AI and Copyright Going Forward
What is clear is that the copyright framework for AI-generated works is still evolving, and just about everyone can agree on that. Like many individuals and organizations, our views may well shift as we learn more about the real-world impacts of generative AI on creative communities and industries. It’s likely that as these policy discussions continue to move forward and policymakers, advocacy groups, and the public alike grapple with the open questions involved, the answers to these open questions will continue to develop. Changes in generative AI technology and the models involved may also influence these conversations. Today, the Copyright Office published issued a notice of inquiry on the topic of copyright in AI-generated works. We plan to submit a comment sharing our perspective, and are eager to learn about the diversity of views on this important issue.
Last week, the District Court for the District of Columbia announced a decision in Thaler v. Perlmutter, a case challenging copyright’s human authorship requirement in the context of a work produced by a generative AI program. This case is one of many lawsuits surrounding copyright issues in generative AI, and surely will not be the last we hear about the copyrightability of AI-generated works, and how this interacts with copyright’s human authorship requirement. In today’s post, we’ll provide a quick summary of the case and offer our thoughts about what this means for authors and other creators.
Background
Back in 2018 (before the current public debate about copyright and generative AI had reached the fever pitch we now see today), Dr. Stephen Thaler applied for copyright registration in a work of visual art produced by a generative AI system he created, called the Creativity Machine. Thaler sought to register his work as a computer-generated “work-made-for-hire” since he created the machine, which “autonomously” produced the work. After a lot of back and forth with the Copyright Office, it maintained its denial of the application, explaining that the human authorship requirement in copyright law foreclosed protection for the AI-generated work, since it was not the product of a human’s creativity.
Then, Thaler then sued Shira Perlmutter, the Register of Copyrights, in the D.C. district court, asking the court to decide “whether a work autonomously generated by an AI system is copyrightable.” Judge Baryl A. Howell upheld the Copyright Office’s decision, explaining that under the plain language of the Copyright Act, “an original work of authorship” required that the author be a human “based on centuries of settled understanding” and a dictionary definition of “author.” She also cited to the U.S. Constitution’s IP clause, which similarly mentions “authors and inventors,” and over a century of Supreme Court precedent to support this principle.
Thaler’s attorney has indicated that he will be appealing the ruling to the D.C. Circuit court of appeals, and it remains to be seen whether that court will affirm the ruling.
Implications for copyright law
The headline takeaway from this ruling is that AI generated art is not copyrightable because copyright requires human authorship, which remains a requirement in copyright law. However, the ruling is actually more nuanced and contains a few subtle points worth highlighting.
For one, this case tested not just the human authorship requirement but also the application of the work-for-hire doctrine in the context of generative AI. On one view of the issues, if Thaler created a machine capable of creating a work that would be copyrightable were it created by a human, there is a certain appeal in framing the work as one commissioned by Thaler. On this point, the court explained that since there was no copyright in the work in the first instance based on its failure to meet the human authorship requirement, this theory also did not hold water. In other words, a work-made-for-hire requires that the “hired” creator also be a human.
It’s important to keep in mind that Thaler was in a sense testing the reach of the limited or “thin” copyright that can be granted in compilations of AI-generated work, or AI-generated work that a human has altered, thus endowing it with at least a modicum of human creativity as copyright requires. Thaler made no changes to the image produced by his Creativity Machine, and in fact, described the process to the Copyright Office as fully autonomous rather than responding to an original prompt (as is generally the case with generative AI). Thaler was not trying to get a copyright in the work in order to monetize it for his own livelihood, but—presumably—to explore the contours of copyright in computer-generated works. In other words, the case has some philosophical underpinnings (and in fact, Thaler has said in interviews that he believes his AI inventions to be sentient, a view that many of us tend to reject). But for creators using generative AI who seek to register copyrights in order to benefit from copyright protection, things are unlikely to be quite so clear-cut. And while she found the outcome to be fairly clear cut in this case, Judge Howell observed:
“The increased attenuation of human creativity from the actual generation of the final work will prompt challenging questions regarding how much human input is necessary to qualify the user of an AI system as an ‘author’ of a generated work, the scope of the protection obtained over the resultant image, how to assess the originality of AI-generated works where the systems may have been trained on unknown pre-existing works, how copyright might best be used to incentivize creative works involving AI, and more.”
What does this all mean for authors?
For authors who want to incorporate AI-generated text or images into their own work, the situation is a bit murkier than it was for Thaler. The case itself provides little in the way of information for human authors who use generative AI tools as part of their own creative processes. But while the Copyright Office’s registration guidance tells creators what they need to do to register their copyrights, this decision provides some insight about what will hold up in court. Courts can and do overturn agency actions in some cases (in this case, the judge could have overturned the Copyright Office’s denial of Thaler’s registration application had she found it to be “arbitrary and capricious”). So the Thaler case in many ways affirms what the Copyright Office has said so far about registrability of AI-generated works, indicating that the Office is on the right track as far as their approach to copyright in AI-generated works, at least for now.
The Copyright Office has attempted to provide more detailed guidance on copyright in “AI-assisted” works, but a lot of confusion remains. One guideline the Office promulgated in a recent webinar on copyright registration in works containing AI-generated material is for would-be registrants to disclose the contribution of an AI system when its contribution is more than “de minimis,” i.e., when the AI-generated creation would be entitled to copyright protection if it were created by a human. This means that using an AI tool to sharpen an image doesn’t require disclosure, but using an AI tool to generate one part of an image does. An author will then receive copyright protection in only their contributions to the work and the changes they made to the AI-generated portions. As Thaler shows, an author must make some changes to an AI-generated work in order to receive any copyright protection at all in that work.
All of this means, broadly speaking, that the more an author changes an AI-generated work—such as by using tools like photoshop to alter an image or by editing AI-generated text—the more likely it is that the work will be copyrightable, and, by the same token, the less “thin” any copyright protection in the work will be. While there are open questions about how much creativity is required from a human in order to transform an AI-generated work into a copyrightable work of authorship, this case has underscored that at least some creativity is required—and using an AI tool that you yourself developed to create the work does not cut it.
The way Thaler framed his Creativity Machine as the creator of the work in question also shows that it is important to avoid anthropomorphizing AI systems—just as the court rejected the notion of an AI-generated work being a work-made-for-hire, a creative work with both generative AI and human contributions probably could not be registered as a “co-authored” work. Humans are predisposed to attribute human characteristics to non-humans, like our pets or even our cars, a phenomenon which we have seenrepeatedly in the context of chat bots. Regardless, it’s important to remember that a generative AI program is a tool based on a model. And thinking of generative AI programs as creators rather than tools can distract us from the established and undisturbed principle in copyright law that only a human can be considered an author, and only a human can hold a copyright.
Last week you may have read about a website called prosecraft.io, a site with an index of some 25,000 books that provided a variety of data about the texts (how long, how many adverbs, how much passive voice) along with a chart showing sentiment analysis of the works in its collection and displayed short snippets from the texts themselves, two paragraphs representing the most and least vivid from the text. Overall, it was a somewhat interesting tool, promoted to authors to better understand how their work compares to those of other published works.
The news cycle about prosecraft.io was about the campaign to get its creator Benji Smith to take the site down (he now has) based on allegations of copyright infringement. A Gizmodo story about it generated lots of attention, and it’s been written up extensively, for example here, here, here, and here.
It’s written about enough that I won’t repeat the whole saga here. However, I think a few observations are worth sharing:
1)Don’t get your legal advice from Twitter (or whatever its called)
“Fair Use does not, by any stretch of the imagination, allow you to use an author’s entire copyrighted work without permission as a part of a data training program that feeds into your own ‘AI algorithm.’” – Linda Codega, Gizmodo (a sentiment that was retweeted extensively)
Fair use actually allows quite a few situations where you can copy an entire work, including situations when you can use it as part of a data training program (and calling an algorithm “AI” doesn’t magically transform it into something unlawful). For example, way back in 2002 in Kelly v. Ariba Soft, the 9th Circuit concluded that it was fair use to make full text copies of images found on the internet for the purpose of enabling web image search. Similarly, in AV ex rel Vanderhye v. iParadigms, the 4th Circuit in 2009 concluded that it was fair use to make full text copies of academic papers for use in a plagiarism detection tool.
Most relevant to prosecraft, in Authors Guild v. HathiTrust (2014) and Authors Guild v. Google (2015) the Second Circuit held that Google’s copying of millions of books for purposes of creating a massive search engine of their contents was fair use . Google produced full-text searchable databases of the works, and displayed short snippets containing whatever term the user had searched for (quite similar to prosecraft’s outputs). That functionality also enabled a wide range of computer-aided textual analysis, as the court explained:
The search engine also makes possible new forms of research, known as “text mining” and “data mining.” Google’s “ngrams” research tool draws on the Google Library Project corpus to furnish statistical information to Internet users about the frequency of word and phrase usage over centuries. This tool permits users to discern fluctuations of interest in a particular subject over time and space by showing increases and decreases in the frequency of reference and usage in different periods and different linguistic regions. It also allows researchers to comb over the tens of millions of books Google has scanned in order to examine “word frequencies, syntactic patterns, and thematic markers” and to derive information on how nomenclature, linguistic usage, and literary style have changed over time. Authors Guild, Inc., 954 F.Supp.2d at 287. The district court gave as an example “track[ing] the frequency of references to the United States as a single entity (‘the United States is’) versus references to the United States in the plural (‘the United States are’) and how that usage has changed over time.”
While there are a number of generative AI cases pending (a nice summary of them is here) that I agree raise some additional legal questions beyond those directly answered in Google Books, the kind of textual analysis that prosecraft.io offered seems remarkably similar to the kinds of things that the courts have already said are permissible fair uses.
2)Text and data mining analysis has broad benefits
Not only is text mining fair use, it also yields some amazing insights that truly “promote the progress of Science,” which is what copyright law is all about. Prosecraft offered some pretty basic insights into published books – how long, how many adverbs, and the like. I can understand opinions being split on whether that kind of information is actually helpful for current or aspiring authors. But, text mining can reveal so much more.
In the submission Authors Alliance made to the US Copyright Office three years ago in support of a Section 1201 Exemption permitting text data mining, we explained:
In social science, studies have used TDM to analyze job advertisements to identify direct discrimination during the hiring process.7 It has also been used to study police officer body-worn camera footage, uncovering that police officers speak less respectfully to Black than to white community members even under similar circumstances.
TDM also shows great promise for drawing insights from literary works and motion pictures. Regarding literature, some 221,597 fiction books were printed in English in 2015 alone, more than a single scholar could read in a lifetime. TDM allows researchers to “‘scale up’ more familiar humanistic approaches and investigate questions of how literary genres evolve, how literary style circulates within and across linguistic contexts, and how patterns of racial discourse in society at large filter down into literary expression.” TDM has been used to “observe trends such as the marked decline in fiction written from a first-person point of view that took place from the mid-late 1700s to the early-mid 1800s, the weakening of gender stereotypes, and the staying power of literary standards over time.” Those who apply TDM to motion pictures view the technique as every bit as promising for their field. Researchers believe the technique will provide insight into the politics of representation in the Network era of American television, into what elements make a movie a Hollywood blockbuster, and into whether it is possible to identify the components that make up a director’s unique visual style [citing numerous letters in support of the TDM exemption from researchers].
3)Text and data mining is not new and it’s not a threat to authors
Text mining of the sort it seemed prosecraft employed isn’t some kind of new phenomenon. Marti Hearst, a professor at UC Berkeley’s iSchool explained the basics in this classic 2003 piece. Scores of computer science students experiment with projects to do almost exactly what prosecraft was producing in their courses each year. Textbooks like Matt Jockers’s Text Analysis with R for Students of Literature have been widely used and adopted all across the U.S. to teach these techniques. Our submissions during our petition for the DMCA exemption for text and data mining back in 2020 included 14 separate letters of support from authors and researchers engaged in text data mining research, and even more researchers are currently working on TDM projects. While fears over generative AI may be justified for some creators (and we are certainly not oblivious to the threat of various forms of economic displacement), it’s important to remember that text data mining on textual works is not the same as generative AI. On the contrary, it is a fair use that enriches and deepensour understanding of literature rather than harming the authors who create it.
UPDATE: On Monday, August 14th, Judge Koeltl issued an order on the proposed judgement, which you can read here, and which this blog post has been updated to reflect. In his order, the judge adopted the definition of “Covered Book” suggested by the Internet Archive, limiting the permanent injunction subject to an appeal to only those books published by the four publisherplaintiffs that are available in ebook form.
After months of deadline extensions, there is finally news in Hachette Books v. Internet Archive, the case about whether Controlled Digital Lending is a fair use, which we have been covering since its inception over two years ago, and in which Authors Alliance filed an amicus brief in support of Internet Archive and CDL. On Friday, August 11th, attorneys for the Internet Archive and a group of publishers filed documents in federal court proposing “an appropriate procedure to determine the judgment to be entered in this case,” as Judge John G. Koeltl of the Southern District of New York requested.
In a letter to the court, both parties indicated that they had agreed to a permanent injunction, subject to an appeal by IA, “enjoining the Internet Archive [] from distributing the ‘Covered Books’ in, from or to the United States electronically.” This means that the Internet Archive has agreed to stop distributing within the U.S. the books in its CDL collection which are published by the plaintiff publishers in the case (Hachette Book Group, HarperCollins, Wiley, and Penguin Random House), and are currently available as ebooks from those publishers. The publishers must also send IA a catalog “identifying such commercially available titles (including any updates thereto in the Plaintiffs’ discretion), or other similar form of notification,” and “once 14 days have elapsed since the receipt of such notice[,]” IA will cease distributing CDL versions of these works under the proposed judgment.
Open Questions
Last week’s proposed judgment did leave an open question, which Judge Koeltl was asked to decide before issuing a final judgment: should IA be enjoined from distributing CDL versions of books published by the four publishers where those books are available in any form, or should it only be enjoined from distributing CDL versions of these books that are available as ebooks? This difference may seem subtle, but it’s actually really meaningful.
The publishers asked for a broader definition, whereby any of their published works that remain in print in any form are off the table when it comes to CDL. The publishers explain in a separate letter to the court that they believe that it would be consistent with the judgment to ban the IA from loaning out CDL versions of any of the commercially available books they publish, whatever the format. They argue that it should be up to the publishers whether or not to issue an ebook edition of the work, and that even when they decide not to do so (based on an author’s wishes or other considerations), IA’s digitization and distribution of CDL scans is still infringement.
On the other hand, the Internet Archive is asked the judge to confine the injunction to books published by the four publishers that are available as ebooks, leaving it free to distribute CDL scans of the publishers’ books that are in print, but only available as print and/or audio versions. It argues that to forbid it from lending out CDL versions of books with no ebook edition available would go beyond the matters at issue in the case—the judge did not decide whether it would be a fair use to loan out CDL versions of books only available in print, because none of the works that the publishers based the suit upon were available only as print editions. Furthermore, IA explains that other courts have found that the lack of availability of a competing substitute (in this case, an ebook edition) weighs in favor of fair use under the fourth factor, which considers market competition and market harm.
It seems to me that the latter position is much more sensible. In addition to CDL scans of books only available as physical books not being at issue in the case, the fair use argument for this type of lending is quite different. One of the main thrusts of the judge’s decision in the case was his argument that CDL scans compete with ebooks, since they are similar products, but this logic does not extend to competition between CDL scans and print books. This is because the markets for digital versions of books and analogue versions of books are quite different. Some readers strongly prefer print versions of books, and some turn to electronic editions for reasons of disability, physical distance from libraries or bookstores, or simple preference. While we believe that IA’s CDL program is a fair use, its case is even stronger when it comes to CDL loans of books that are not available electronically.
Then on Monday, August 14th, Judge Koeltl issued an order and final judgment in the case, agreeing with the Internet Archive and limiting the injunction to books published by the four publishers which are available in ebook form. Again, this may seem minor, but I actually see it as a substantial win, at least for now. While even the more limited injunction is a serious blow to IA’s controlled digital lending program, it does allow them to continue to fill a gap in available electronic editions of works. The judge’s primary reasoning was that books not available as ebooks was beyond the scope of what was at issue in the case, but he also mentioned that factor four analysis could have been different were there no ebook edition available.
Limitations of the Proposed Judgment
Importantly, the parties also stipulated that this injunction is subject to an appeal by the Internet Archive. This means that if the Internet Archive appeals the judgment (which it has indicated that it plans to do), and the appeals court overturns Judge Koeltl’s decision, for example by finding that its CDL program is a fair use, IA may be able to resume lending out those CDL versions of books published by the plaintiffs which are also available as ebooks. The agreement also does not mean that IA has to end its CDL program entirely—neither books published by other publishers nor books published by the publisher plaintiffs that are not available as ebooks are covered under the judge’s order.
What’s Next?
The filing represents the first step towards the Internet Archive appealing the court’s judgment. As we’ve said before, Authors Alliance plans to write another amicus brief in support of the Internet Archive’s argument that Controlled Digital Lending is a fair use. Now that the judge has issued his final judgment, IA has 30 days to file a “notice of appeal” with the district court. Then, the case will receive a new docket in the Second Circuit Court of Appeals, and the various calendar and filing processes will begin anew under the rules of that court. We will of course keep our readers apprised of further developments in this case.
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.
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.
We think this is a fantastically bad idea. One of the most important developments in the movement for open access to scholarship came last year when Dr. Alondra Nelson, Director of the Office of Science and Technology Policy, issued a memorandum mandating that all federal agencies that sponsor research put in place policies to ensure immediate open access to published research, as well as access to research data. The agencies are at various stages of implementing the Nelson memo now, but work is well underway. This appropriations bill specifically targets those implementation efforts and would prevent any federal government expenditures from being used to further them.
For the vast majority of scholarly works, the primary objective of the authors is to share their research as widely as possible. Open access achieves that for authors (if they can only get their publishers to agree). The work is already funded, already paid for. As you might imagine, those opposed to the memo are primarily publishers who have resisted adapting the business model they’ve built of putting a paywall in front of publicly-funded work, largely for profit.
Thankfully, the CJS appropriations bill, one of twelve appropriation bills, is just a first crack at how to fund the government in the coming year. The Senate, of course, will have their say, as will the President. With the current division in Congress, combined with the upcoming recess (Congress will be on recess in August and reconvene in September), the smart bet is that none of these bills will be enacted in time for the federal government’s new fiscal year on October 1. Instead, a continuing resolution–funding the government under the status quo, as Congress frequently does–will likely be enacted as a stop gap until a compromise can be reached later in the year.
It is important, however, that legislators understand that this attempt to defund OA efforts is majorly concerning, especially for authors, universities, and libraries that believe that federally funded research should be widely available on an open access basis. It’s a good moment to speak out. SPARC has put together a helpful issue page on this bill, complete with sample text for how to write to your representative or senator.
As you’ll see if you read the proposed appropriations bill, it is loaded with politics. The relevant OSTP memo language is located amongst other clauses that defund the Biden administration’s efforts to implement diversity initiatives at various agencies, address gun violence, sue states over redistricting, and dozens of other hot-button issues as well. It’s pretty easy for an issue like access to science to get lost in the political shuffle, but we hope with some attention from authors and others, it won’t.
On Monday, the Ninth Circuit issued a decision in Hunley v. Instagram, a case about whether Instagram (and platforms like it) can be held liable for secondary infringement based on its embedding feature, whereby websites employ code to display an Instagram post on their sites within their own content. We are delighted to announce that the court ruled in favor of Instagram, reinforcing important copyright principles which allow authors and other creators to link to and embed third-party content, enriching their writing in the process.
Our Brief
Authors Alliance signed on to an amicus brief in this case, arguing that Instagram should not be held liable for contributory infringement for its embedding feature. We explained that Instagram was not liable under a precedential legal test established in Perfect 10 v. Amazon, and moreover that a ruling to the contrary could place our ability to link to other online content (which is analogous to embedding in many ways) at risk for legal liability.
Narrowing the Perfect 10 test—which establishes that a website does not infringe when it does not store a copy of the relevant work on its server—would have struck a blow to how we share and engage with online content. Linking to other information allows authors to easily cite to other information without disrupting the flow of their writing. By the same token, it allows internet users to verify information and learn more about topics of interest, all with the click of a button. We are pleased that the court ruled in favor of Instagram, declining to revisit the Perfect 10 test and holding that it foreclosed relief for the photographers that had filed the lawsuit. In so doing, the court has helped maintain a vibrant internet where all can share and engage with knowledge and creative expression.
The Decision
The case concerned a group of photographers whose instagram posts were embedded into content by several media outlets. The photographers then sued Instagram in the Northern District of California, on the theory that by offering the “embedding” feature, it was facilitating copyright infringement of others and therefore was liable. The district court found that Perfect 10 applied to the case, and therefore that Instagram was not liable for infringement for the outlets’ display of the posts.
The Ninth Circuit agreed, and furthermore declined to revisit or narrow the Perfect 10 case for a number of reasons—it rejected the argument that the search engines at issue in the Perfect 10 case itself were somehow different from social media platforms, and affirmed that Perfect 10 was consistent with more recent Supreme Court case law. The court also cited with approval our argument that embedding and in-line linking have paved the way for innovation and creativity online, though did not adopt the justification, reasoning that it is not a court’s job to serve as a policymaker. In applying the Perfect 10 test, the court explained that Instagram did not infringe the photographers’ copyrights, and where there is no direct infringement, there cannot be related secondary infringement. Instagram displayed a copy of the relevant photographs on its platform, which users permit via a license they agree to by using the platform. But it did not facilitate the images’ display elsewhere, because the computer code used by the media platforms that embedded the instagram posts did not make a copy of the posts, but rather formatted and displayed them.
Authors rarely have meaningful rights to say how their publisher licenses or distributes their book.
A typical publishing contract will grant the publisher broad discretion to determine the format, price and sublicensing terms under which an author’s book is made available. It can be hard to negotiate for the right to have a say over those terms. Even contracts designed to prioritize authors’ rights, such as the Authors Guild model trade contract, don’t contemplate an author exercising much control over these matters, and leave most publication and distribution details “as Publisher determines.”
In many cases, ceding control can be OK as long as the interests of the publisher and author are tightly aligned. It’s why we recommend authors pay close attention to the mission and practices of their publisher before signing a contract. But even when a publisher purports to share the author’s interests, this could change in the future, and information the publisher provides about itself can be misleading.
Sometimes, it’s hard to see how those interests diverge until it’s too late. For example, recall last year when academic publisher Wiley decided to remove some 1,300 ebooks from online library collections. We quickly found that many authors of those books objected strongly, and joined us in a letter that outlined concerns and expressed dismay that Wiley, an academic publisher that supposedly prioritizes” access to knowledge,” would make such an aggressive and profit-maximizing decision. But under their contracts, those authors had no legal grounds to push back.
Library distribution in particular is an area of concern. Libraries provide an important way for authors to connect with readers, and provide a means of access to their books for many people who might otherwise never read them. Libraries also serve an important democratic function in supporting widespread learning that we all benefit from. We’ve written severaltimes over the years about challenges that libraries face in licensing ebooks, and it’s why we’ve supported model state legislation to address the problem and also why we’ve supported models like controlled digital lending that allow for limited access outside of the licensing model.
In addition to basic economic concerns about gouging libraries on price (in some cases publishers have decided to charge libraries 10x the consumer list price for ebook access), some publishers have imposed a variety of other terms that we find unreasonable. This includes, for example, only offering ebooks to libraries through large bundles of content rather than title-by-title, which forces libraries to buy access to books that aren’t necessarily relevant for their community (a practice which also obfuscates and dilutes per-title sales and consequently author royalties). Or limiting access for use only on platforms controlled by the publisher, which can contain significant compromises for reader privacy. Perhaps the most frustrating is the flat refusal to deal – with some publishers refusing to sell some ebooks to libraries at all, in the hopes of driving some would-be library readers (likely a very small percentage of them) into buying a personal copy.
Introducing the Library Ebook Pledge
What libraries need to do their jobs in the digital environment isn’t all that complicated. For physical books, libraries have been successful in reaching readers because they have had clear rights to purchase, lend, and preserve. Publishers have limited, by contract, libraries’ ability to do those same activities with ebooks, but it doesn’t have to be that way. That’s why we’ve been pleased to work with Knowledge Rights 21 and Library Futures to outline twelve basic principles that represent a reasonable approach to ensuring that libraries can continue to do their jobs online.
We know that many publishers care deeply about the role of libraries in supporting research, education and learning. This Pledge, which can be viewed here, offers a way for those publishers to express their support and commitment to 21st century libraries, so libraries can provide meaningful preservation of and access to ebooks for their readers. We’re encouraged to see some publishers already signing on, and encourage others to do so as well.
We also think this pledge is a valuable tool for authors who care about access to their works. While negotiating for control over distribution can be a challenge, we are hopeful that authors can try to incorporate these principles into their contracts and use this pledge to ask publishers to publicly communicate their intent to license ebooks in ways that will account for the public interest.
