Tag Archives: Copyright

Revived Class Action Against McGraw Hill: the Importance of Publishing Contracts

Posted November 15, 2024

open book with glasses on top

On November 6th, the 2nd Circuit Court of Appeals overturned the lower court’s dismissal in Flynn v. McGraw Hill, and allowed the plaintiffs’ breach of contract claim to move forward. 

The breach of contract claim involves McGraw Hill’s alleged practice of reducing or ceasing royalty payments on revenues generated through McGraw Hill’s online platform, Connect, which hosts electronic textbooks and related course materials since its launch in 2009. The publishing contracts at issue specified that McGraw Hill would publish the plaintiffs’ textbooks “at its own expense” and that royalties would be based on “Publisher’s net receipts”—defined mostly as “the Publisher’s selling price, less discounts, credits, and returns, or a reasonable reserve for returns;” although the initially signed contracts only covered print works, McGraw Hill later amended the contracts to cover electronic works under the same royalties structure. McGraw Hill paid royalties based on the entire revenue from ebook sales through Connect, which included both the ebook and its accompanying materials such as PowerPoint lesson plans and test banks.

This changed in 2020, according to the plaintiffs, when McGraw Hill started paying royalties solely on sales attributed to the ebooks, excluding the revenue derived from the accompanying materials, despite the fact that the accompanying materials cannot be bought independent of the ebook. Under the new practice, McGraw Hill would unilaterally determine which part of the revenue is attributable to the ebooks, their accompanying materials, or the Connect platform, even though the sales are always based on a “single unitary price”.

The plaintiffs argue that this new arrangement violated McGraw Hill’s promise to publish the works “at its own expense,” a provision that should have meant authors wouldn’t be charged for the cost of operating or maintaining the publisher’s infrastructure; this claim is now allowed to go forward. The claim related to “net receipts” was again dismissed.

While the ongoing developments in this case are worth watching closely, it also serves as a timely reminder—especially in light of publishers’ licensing content for AI training—for authors to carefully review and negotiate their publishing agreements, and to rely on the contractual terms that hold publishers accountable to their promises.

Let’s take this opportunity to quickly remind ourselves of a couple of less-discussed contractual terms that may in fact be too important to ignore.

1. “…media now known and may be developed in the future”

The harm plaintiffs are claiming, in this case, is a whopping 25% to 35% drop in royalties when works are published on McGraw Hill’s online platform. Although this case only arose out of the electronic rights of textbooks, it reminds us how the advent of new technology could easily undermine instead of boost the income of authors.

Barely a decade ago, most experts of the publishing industry believed that the economics of e-book publishing were more favorable to publishers, as e-books are cheaper to produce than print books. As a result, authors should expect to receive a much larger share of the revenue—well above the typical 10-15% of the retail price for trade books.

The Flynn case confirms many authors’ suspicion that authors may not necessarily share in the financial boon brought by new technologies. It is thus important for authors to be wary of a broad copyright license that allows all future technology for disseminating the authors’ works. 

It’s worth reviewing terms that address the publisher’s ability to license your works in specific contexts, including digital platforms and emerging technology that are not named. Instead of “media now known and may be developed in the future,” authors should consider limiting the publication of their works to specific, enumerated media, such as print books or ebooks. Failing that, authors should propose alternative terms that could safeguard their interests, such as a clause that allows for rights reversion if royalties fall below a certain level.

2. Royalty Audit

A common feature of publishing contracts is a clause that allows authors to audit the publisher’s accounting. While it may not seem like a top priority at first glance, authors should absolutely take advantage of this provision if it’s included in their agreement. An audit right provides authors with the legal right to review the publisher’s financial records to verify whether they are being compensated fairly and according to the terms of the contract.

Authors in the Flynn case learned about the new royalties arrangement through an email from the publisher. It is of course important for authors to monitor any communications sent by their publishers. However, it is not certain that publishers will always disclose it when they adopt a new method of calculating royalties, and certainly not a given that their accounting never makes any mistake. When authors become suspicious of their publisher’s deductions or other financial practices, the ability to audit can be crucial. Publishers may make deductions or shift expenses that are not immediately obvious to authors based on the royalties they receive. An audit can help uncover if a publisher is deducting expenses that are unjustified (such as fees for maintaining online systems, as in this case). The audit right can be an essential tool for discovering accounting discrepancies and ensuring the publisher is acting in good faith.

As generative AI tools become more prevalent, many authors are concerned about how their works may be used for AI training without their knowledge or consent. It’s important to remember that not all contracts automatically grant publishers or other entities the right to license works for use in AI training. If you have retained sublicensing rights, or your publishing contract offers a broader definition of net receipts or profits, you could be entitled to the revenue your publishers earned from selling your works to train AI. 

Just as with traditional royalties, income from AI licensing should be distributed according to the terms of the contract. If you’re uncertain about whether you are getting fairly compensated, don’t hesitate to utilize the auditing right to request detailed information from your publisher.

Final Thoughts: Be Proactive and Stay Informed

At the heart of the Flynn v. McGraw Hill case is a breach of contract claim. The plaintiffs argue that McGraw Hill’s royalty deductions for maintaining its online system violated the terms of the agreement. Central to the argument is the publisher’s promise to ‘publish at its own expense.’ This case serves as a prime example of how important it is to scrutinize the details of a publishing agreement, where the devil often lies.

Many publishing agreements are complex and may contain clauses that, while seemingly minor, can have significant financial and creative consequences. It’s essential that authors take the time to review their contracts thoroughly, ideally consulting with colleagues and mentors who have more extensive experience with similar situations, to fully understand—at the very least—how their income will be calculated and what rights they are granting to the publisher.

The DMCA 1201 Rulemaking: Summary, Key Takeaways, and Other Items of Interest

Posted November 8, 2024

Last month, we blogged about the key takeaways from the 2024 TDM exemptions recently put in place by the Librarian of Congress, including how the 2024 exemptions (1) expand researchers’ access to existing corpora, (2) definitively allow the viewing and annotation of copyrighted materials for TDM research purposes, and (3) create new obligations for researchers to disclose security protocols to trade associations. Beyond these key changes, the TDM exemptions remain largely the same: researchers affiliated with universities are allowed to circumvent TPMs to compile corpora for TDM research, provided that those copies of copyrighted materials are legally obtained and adequate security protocols are put in place.

We have since updated our resources page on Text and Data Mining and have incorporated the new developments into our TDM report: Text and Data Mining Under U.S. Copyright Law: Landscape, Flaws & Recommendations.

In this blog post, we share some further reflections on the newly expanded TDM exemptions—including (1) the use of AI tools in TDM research, (2) outside researchers’ access to existing corpora, (3) the disclosure requirement, and (4) a potential TDM licensing market—as well as other insights that emerged during the 9th triennial rulemaking.

The TDM Exemption

In other jurisdictions, such as the EU, Singapore, and Japan, legal provisions that permit “text data mining” also allow a broad array of uses, such as general machine learning and generative AI model training. In the US, exemptions allowing TDM so far have not explicitly addressed whether AI could be used as a tool for conducting TDM research. In this round of remaking, we were able to gain clarity on how AI tools are allowed to aid TDM research. Advocates for the TDM exemptions provided ample examples of how machine learning and AI are key to conducting TDM research and asked that “generative AI” not be deemed categorically impermissible as a tool for TDM research. The Copyright Office agreed that a wide array of tools could be utilized for TDM research under the exemptions, including AI tools, as long as the purpose is to conduct “scholarly text and data mining research and teaching.” The Office was careful to limit its analysis to those uses and not address other applications such as compiling data—or reusing existing TDM corpora—for training generative AI models; those are an entirely separate issue from facilitating non-commercial TDM research.

Besides clarifying that AI tools are allowed for TDM research and that viewing and annotation are permitted for copyrighted materials, the new exemptions offer meaningful improvement to TDM researchers’ access to corpora. The previous 2021 exemptions allowed access for purposes of “collaboration,” but many researchers interpreted that narrowly, and the Office confirmed that “collaboration” was not meant to encompass outside research projects entirely unrelated to the original research for which the corpus was created. Under the 2021 exemptions, a TDM corpus could only be accessed by outside researchers if they are working on the same research project as the original compiler of the corpus. The 2024 exemptions’ expansion of access to existing corpora has two main components and advantages. 

The expansion now allows for new research projects to be conducted on existing corpora, permitting institutions that have created a corpus to provide access “to researchers affiliated with other nonprofit institutions of higher education, with all access provided only through secure connections and on the condition of authenticated credentials, solely for purposes of text and data mining research or teaching.” At the same time, it also opens up new possibilities for researchers at institutions who otherwise would not have access, as the new exemption does not require a precondition that the outside researchers’ institutions otherwise own copies of works in the corpora. The new exemptions pose some important limitations: only researchers at institutions of higher education are allowed this access, and nothing more than “access” is allowed—it does not, for example, allow the transfer of a corpus for local use. 

The Office emphasized the need for adequate security protections, pointing back to cases such as Authors Guild v. Google and Authors Guild v. HathiTrust, which emphasized how careful both organizations were, respectively, to prevent their digitized corpora from being misused. To take advantage of this newly expanded TDM exemption, it will be crucial for universities to provide adequate IT support to ensure that technical barriers do not impede TDM researchers. That said, the record for the exemption shows that existing users are exceedingly conscientious when it comes to security. There have been zero reported instances of security breaches or lapses related to TDM corpora being compiled and used under the exemptions. 

As we previously explained, the security requirements are changed in a few ways. The new rule clarifies that trade associations can send inquiries on behalf of rightsholders. However, inquiries must be supported by a “reasonable belief” that the sender’s works are in a corpus being used for TDM research. It remains to be seen how the new obligation to disclose security measures to trade associations would impact TDM researchers and their institutions. The Register circuitously called out demands by trade associations sent to digital humanities researchers in the middle of the exemption process with a two-week response deadline as unreasonable and quoted NTIA (which provides input on the exemptions) in agreement that  “[t]he timing, targeting, and tenor of these requests [for institutions to disclose their security protocols] are disturbing.”  We are hopeful that this discouragement from the Copyright Office will prevent any future large-scale harassment towards TDM researchers and their institutions, but we will also remain vigilant in case trade associations were to abuse this new power. 

Alongside the concerns over disclosure requirements, we have some questions about the Copyright Office’s treatment of fair use as a rationale for circumventing TPMs for TDM research. The Register restated her 2021 conclusion that “under Authors Guild, Inc. v. HathiTrust, lost licensing revenue should only be considered ‘when the use serves as a substitute for the original.’” The Office, in its recommendations, placed considerable weight on the lack of a viable licensing market for TDM, which raises a concern that, in the Office’s view, a use that once was fair and legal might lose that status when the rightsholder starts to offer an adequate licensing option. While this may never become a real issue for the existing TDM exemptions (because no sufficient licensing options exist for TDM researchers, and for the breadth and depth of content needed, it seems unlikely to ever develop), it nonetheless contributes to the growing confusion surrounding the stability of a fair use defense in the face of new licensing markets. 

These concerns highlight the need for ongoing advocacy in the realm of TDM research. Overall, the Register of Copyright recognizes TDM as “a relatively new field that is quickly evolving.” This means that we could ask the Library of Congress to relax the limitations placed on TDM if we can point to legitimate research-related purposes. But, due to the nature of this process, it also means TDM researchers do not have a permanent and stable right to circumvent TPMs. As the exemptions remain subject to review every three years, many large trade associations advocate for the TDM exemptions to be greatly limited or even canceled, wishing to stifle independent TDM research. We will continue to advocate for TDM researchers, as we did during the 8th and 9th triennial rulemaking. 

Looking beyond the TDM exemption, we noted a few other developments: 

Warhol has not fundamentally changed fair use

First, the Opponents of the renewal of the existing exemptions repeatedly pointed to Warhol Foundation v. Goldsmith—the Supreme Court’s most recent fair use opinion—to argue that it has changed the fair use analysis such that the existing exemptions should not be renewed. For example, the Opponents argued that the fair use analysis for repairing medical devices changed under Warhol because, according to them, commercial nontransformative uses were less likely to be fair. The Copyright Office did not agree. The Register said that the same fair use analysis as in 2021 applied and that the Opponents failed “to show that the Warhol decision constitutes intervening legal precedent rendering the Office’s prior fair use analysis invalid.” In another instance where the Opponents tried to argue that commerciality must be given more weight under Warhol, the Register pointed out that under Warhol commerciality is not dispositive and must be weighed against the purpose of the new use.  The arguments for revisiting the 2021 fair use analyses were uniformly rejected, which we think is good news for those of us who believe Warhol should be read as making a modest adjustment to fair use and not a wholesale reworking of the fair use doctrine. 

Does ownership and control of copies matter for access? 

One of the requests before the Office was an expansion of an exemption that allows for access to preservation copies of computer programs and video games. The Office rejected the main thrust of the request but, in doing so, also provided an interesting clarification that may reveal some of the Office’s thinking about the relationship between fair use and access to copies owned by the user: 

The Register concludes that proponents did not show that removing the single user limitation for preserved computer programs or permitting off-premises access to video games are likely to be noninfringing. She also notes the greater risk of market harm with removing the video game exemption’s premises limitation, given the market for legacy video games. She recommends clarifying the single copy restriction language to reflect that preservation institutions can allow a copy of a computer program to be accessed by as many individuals as there are circumvented copies legally owned.”

That sounds a lot like an endorsement of the idea that the owned-to-loaned ratio, a key concept in the controlled digital lending analysis, should matter in the fair use analysis (which is something the Hachette v. Internet Archive controlled digital lending court gave zero weight to). For future 1201 exemptions, we will have to wait and see whether the Office will use this framework in other contexts. 

Addressing other non-copyright and AI questions in the 1201 process

The Librarian of Congress’s final rule included a number of notes on issues not addressed by the rulemaking: 

“The Librarian is aware that the Register and her legal staff have invested a great deal of time over the past two years in analyzing the many issues underlying the 1201 process and proposed exemptions. 

Through this work, the Register has come to believe that the issue of research on artificial intelligence security and trustworthiness warrants more general Congressional and regulatory attention. The Librarian agrees with the Register in this assessment. As a regulatory process focused on technological protection measures for copyrighted content, section 1201 is ill-suited to address fundamental policy issues with new technologies.” 

Proponents tried to argue that the software platforms’ restrictions and barriers to conducting AI research, such as their account requirements, rate limits, and algorithmic safeguards, are circumventable TPMs under 1201, but the Register disagreed. The Register maintained that the challenges Proponents described arose not out of circumventable TPMs but out of third-party controlled Software as a Service platforms. This decision can be illuminating for TDM researchers seeking to conduct TDM research on online streaming media or social media posts.

The Librarian’s note went on to say: “The Librarian is further aware of the policy and legal issues involving a generalized ‘‘right to repair’’ equipment with embedded software. These issues have now occupied the White House, Congress, state legislatures, federal agencies, the Copyright Office, and the general public through multiple rounds of 1201 rulemaking. 

Copyright is but one piece in a national framework for ensuring the security, trustworthiness, and reliability of embedded software, as well as other copyright-protected technology that affects our daily lives. Issues such as these extend beyond the reach of 1201 and may require a broader solution, as noted by the NTIA.”

These notes give an interesting, though a bit confusing, insight into how the Librarian of Congress and the Copyright Office think about the role of 1201 rulemaking when they address issues that go beyond copyright’s core concerns. While we can agree that 1201 is ill-suited to address fundamental policy issues with new technology, it is also somewhat concerning that the Office and the Librarian view copyright more generally as part of a broader “national framework for ensuring the security, trustworthiness, and reliability of embedded software.”  While of course, copyright is sometimes used to further ends outside of its intended purpose, these issues are far from the core constitutional purpose of copyright law and we think they are best addressed through other means. 

Copyright Management Information, 1202(b), and AI

Posted October 30, 2024

This post is by Maria Crusey, a third-year law student at Washington University in St. Louis. Maria has been working with Authors Alliance this semester on a project exploring legal claims in the now 30+ pending copyright AI lawsuits. 

In the recent spate of copyright infringement lawsuits against AI developers, many plaintiffs allege violations of 17 U.S.C. § 1202(b) in their use of copyrighted works for training and development of AI systems.  

Section 1202(b) prohibits the “removal or alteration of copyright management information.” Compared to related provisions in 17 U.S.C. § 1201, which protects against circumvention of copyright protection systems, §1202(b) has seldom been litigated at the appellate level, and there’s a growing divide among district courts about whether §1202(b) should apply to derivative works, particularly those created using AI technology.

At first glance, §1202(b) appears to be a straightforward provision. However, the uptick in §1202(b) claims raises some challenging questions, namely: How does §1202(b) apply to the use of a copyrighted work as part of a dataset that must be cleaned, restructured, and processed in ways that separate copyright management information from the content itself? And how should 1202(b) apply to AI systems that may reproduce small portions of content contained in training data?  Answers to this question may have serious implications in the AI suits because violations of 1202(b) can come with hefty statutory damage awards – between $2,500 and $25,000 for each violation. Spread across millions of works, the damages could be staggering. How the courts resolve this issue could also impact many other reuses of copyrighted works–from analogous uses such as text data mining research to much more routine re-distribution of copyrighted works in other contexts. 

One of these AI cases has requested that the Ninth Circuit Court of Appeals accept an interlocutory appeal on just this issue, and we are waiting to see whether the court will accept it.

For an introduction to §1202(b) and observations on this question, among others, read on:

What is § 1202(b) and what is it intended to do?

Broadly, 17 U.S.C. § 1202 is a provision of the Digital Millennium Copyright Act (DMCA) that protects the integrity of copyright management information (“CMI”). Per §1202(c), CMI comprises certain information identifying a copyrighted work, often including the title, the name of the author, and terms and conditions for the use of a work.

Section 1202(b) forbids the alteration or removal of copyright management information. The section provides that:

“[n]o person shall, without the authority of the copyright owner or the law – 

(1) intentionally remove or alter any CMI,

(2) distribute or import for distribution CMI knowing that the CMI has been removed or altered without authority of the copyright owner or the law, or 

(3) distribute, import for distribution, or publicly perform works, copies of works or phonorecords, knowing that copyright management information has been removed or altered without authority of the copyright owner or the law, knowing, or with respect to civil remedies under section 1203, having reasonable grounds to know that it will induce, enable, facilitate, or conceal an infringement of any right under this title.”

17 U.S.C. § 1202(b).

Congress primarily aimed to limit the assistance and enablement of copyright infringement in its enactment of §1202(b). This purpose is evident in the legislative history of the provision. In an address to a congressional subcommittee prior to the adoption of the DMCA, the then–Register of Copyrights, Marybeth Peters, discussed the aims of §1202(b). First, Peters noted that the requirements of §1202(b) would make CMI more reliable and thus aid in the administrability of copyright law. Second, Peters stated that §1202(b) would help prevent instances of copyright infringement that could come from the removal of CMI. The idea is if a copyrighted work lacks CMI, there is a greater likelihood of infringement since others may use the work under the pretense that they are the author or copyright holder. In creating a statutory violation for a party’s removal of CMI, regardless of later infringing activity, §1202(b) functions as damage control against potential copyright infringement.

What are the essential elements of a § 1202(b) claim?

To have a claim under §1202(b), a plaintiff must allege particularized facts about the existence and alteration or removal of CMI. Additionally, some courts require a plaintiff to demonstrate that the defendant had knowledge that the CMI was being altered or removed and that the alteration or removal would enable copyright infringement. Finally, some courts have required plaintiffs to show that the work with the altered or removed CMI is an exact copy of the original work–what has become known as the “identicality” requirement. This last “identicality” requirement is one of the main issues in the AI lawsuits raising §1202(b) and is detailed further below.

→ The “Identicality” Requirement

Courts that have imposed “identicality” have required that plaintiffs demonstrate that the work with the removed CMI is an exact copy of the original work and thus is “identical,” except for the missing or altered CMI. 

Suppose, for example, a photographer owns the copyright to a photograph they took. The photographer adds CMI to the photograph and takes care to protect the integrity of the work as it is dispersed online. A third party captures the photograph posted on a website by taking a screenshot and removes the CMI from the copied image while keeping all other aspects of the original photograph the same. The screenshot with the removed CMI is an “exact copy” of the original photograph because the only difference between the copyrighted photograph and the screenshot is the removal of the CMI.

Federal courts are divided in imposing the identicality requirement for §1202(b) claims, though the circuit courts have not yet addressed the issue. Notably, district courts of the Ninth Circuit Court of Appeals have varied in their treatments of the identicality requirement. For example, the court for the District of Nevada in Oracle v. Rimini Street declined to impose the identicality requirement because the requirement may weaken the intended protections for copyright holders under §1202(b). Conversely, in Kirk Kara Corp. v. W. Stone & Metal Corp., a court in the Central District of California applied the identicality requirement, though it provided little explanation for why it adopted it. Application of the identicality requirement is also unsettled in district courts beyond the Ninth Circuit (see, for example, this Southern District of Texas case discussing at length the identicality requirement and rejecting it). 

What are the §1202(b) claims at issue in the present suits?

The claims in Doe 1 v. Github exemplify the §1202(b) issues common among the present suits, and it is the Github suit that is presently before the Ninth Circuit Court of Appeals to take, if it wishes, on appeal.  

In Github, owners of copyrights in software code brought a suit against GitHub, a software developer platform. The plaintiffs alleged that Microsoft Copilot, an AI product developed in part by GitHub, illegally removed CMI from their works. The plaintiffs stored their software in GitHub’s publicly accessible software repositories under open-source license agreements. The plaintiffs claimed that GitHub removed CMI from their code and trained the Copilot AI model on the code in violation of the license agreements. Moreover, the plaintiffs claimed that, when prompted to generate software code, Copilot includes unique aspects of the plaintiffs’ code in its outputs. In their complaint, the plaintiffs alleged that all requirements for a valid § 1202(b) claim were met in the present suit. The plaintiffs stressed that, in removing CMI, the defendants failed to prevent users of products from making non-infringing use of the product. Consequently, they claim, the defendants removed the CMI, knowing that it would “induce, enable, facilitate, and/or conceal infringement” of copyrights in violation of the DMCA.

Regarding the §1202(b) claims, the parties contest the application of the identicality requirement. The plaintiffs first argue that § 1202 contains no such requirement: “The plain language of DMCA § 1202 makes it a violation to remove or alter CMI. It does not require that the output work be original or identical to obtain relief. . . By a plain reading of the statute, there is no need for a copy to be identical—there only needs to be copying, which Plaintiffs have amply alleged.” 

As a backstop, the plaintiffs further argue that Copilot does produce “near-identical reproduction[s]” of their copyrighted code and allege this is sufficient to fulfill the identicality requirement under §1202(b). Specifically, plaintiffs claimed that Copilot generates parts of plaintiffs’ code in extra lines of output code that are not relevant to input prompts. Plaintiffs also claimed Copilot generates their code in output code that produces errors due to a mismatch between the directly copied code and the code that would actually fit the prompt. To make this assertion work, plaintiffs distinguish their version of “identicality” –semantically equivalent lines of code–from a reproduction of the whole work. They argue that the defendant’s position, that “the reproduction of short passages that may be part of [a] larger work, rather than the reproduction of an entire work, is insufficient to violate Section 1202,” would lead to absurd results. “By OpenAI’s logic, a party could copy and distribute a fragment of a copyrighted work—say, a chapter of a book, a stanza of a poem, or a scene from a movie—and face no repercussions for infringement.” 

 In their reply, the defendants countered that §1202, which defines CMI as relating to a “copy of a work,” requires a complete and identical copy, not just snippets. Defendants noted that the plaintiffs have conceded that Copilot reproduces only snippets of code rather than complete versions of the code. Therefore, the defendants argue, Copilot does not create “identical copies” of the plaintiffs’ complete copyrighted works. The argument is based on both the text of the statute (they note that the statute only provides for liability when distributing copies that CMI has been stripped from, not derivatives, abridgments, or other adaptations), and they bolster those arguments by suggesting that allowing 1202 claims for incomplete copies would create chaos for ordinary uses of copyrighted works: “On Plaintiffs’ reading of § 1202, if someone opened an anthology of poetry and typed up a modified version of a single “stanza of a poem,” . . . without including the anthology’s copyright page, a § 1202(b) claim would lie. Plaintiffs’ reading effectively concedes that they are attempting to turn every garden-variety claim of copyright infringement into a DMCA claim, only without the usual limitations and defenses applicable under copyright law. Congress intended no such thing.” 

The GitHub court has addressed the issue now several times: it initially dismissed the plaintiffs’ §1202(b)(1) and (b)(3) claims, subsequently denied the plaintiffs’ motion for reconsideration of the claims, allowed the plaintiffs to amend their complaint and try again with more specificity, then dismissed the claims again. The reasoning of the court has been consistent, and largely focused on insufficient allegations of identicality. The court agreed with Defendants that the identicality requirement should apply and that the snippets do not satisfy the requirement. Following the dismissal, the plaintiffs sought and received permission from the district court to file an interlocutory appeal (an appeal on a specific issue before the case is fully resolved– something not usually allowed) to the Court of Appeals for the Ninth Circuit to determine whether § 202(b)(1) and (b)(3) impose an identicality requirement. The Ninth Circuit is presently considering whether to hear the appeal.

What would the Ninth Circuit assess in the appeal, and what are the implications of the appeal for future lawsuits?

If the appeal is accepted, the Ninth Circuit will determine whether §1202(b)(1) and (b)(3) actually impose an identicality requirement. Moreover, with regard to the facts of the Github case, the court will decide whether the identicality requirement requires exact copying of a complete copyrighted work, or perhaps something less. The Ninth Circuit’s hearing of this appeal would be notable for a number of reasons.

First, as mentioned above, §1202(b) is largely unaddressed by the circuit courts, and explicit appellate guidance has only been provided for the knowledge requirement referenced above. Consequently, determinations of §1202(b) claims are largely informed by varying district court decisions that are binding only on the parties to the suits and provide inconsistent interpretations of the requirements for a claim under the provision. An appellate ruling that accepts or rejects the identicality requirement would create additional binding authority to further clarify courts’ interpretations of §1202(b).

Second, a ruling on the identicality requirement from the Ninth Circuit specifically would be notable because it would be binding on the large number of §1202(b) claims presently being litigated in the Ninth Circuit’s lower courts. And, given the centrality of AI developers operating in California and elsewhere in the Ninth Circuit, the outcome of the appeal would significantly impact future lawsuits that involve §1202(b) claims.

It is hard to predict how the Ninth Circuit might rule, but we can work through some of the implications of the choices the court would have before it: 

If the Ninth Circuit interprets the identicality requirement as requiring a complete and exact copy, it would impose a high standard for the requirement and plaintiffs would likely be constrained in their ability to bring §1202(b) claims. If the court did this, the Github plaintiffs’ claims would likely fail as the alleged copied snippets of code generated by Copilot are not exact copies and do not comprise the complete copyrighted works. This hypothetical standard would be advantageous for individuals who remove CMI from copyrighted works in the course of processing them using AI as well as those who deploy AI systems that produce small portions of content similar (but not exactly so) to inputs.  So long as the works being processed or distributed are not complete exact copies, individuals would be free to alter the CMI of the works for ease in analyzing the copyrighted information. 

Alternatively, the Ninth Circuit could adopt a loose interpretation of identicality in which incomplete and inexact copying would be sufficient. One approach would be to require identicality but not copying of the entire work (something the plaintiffs in the Github suit advocate for). How the parties or the Ninth Circuit would formulate what standard would apply to this “less than entire” but still “near identical” standard is hard to say, but presumably, plaintiffs would have an easier time alleging facts sufficient for a §1202(b) claim. Applied to Github, it still seems unclear that the copied snippets of the plaintiffs’ code in the Copilot outputs could pass muster (this is likely a factual question to be determined at later stages of the litigation). But it could allow claims to at least survive an early motion to dismiss. As such, the adoption of this standard could limit how AI developers engage with works but also potentially affect others, such as researchers using similar techniques to process, clean, and distribute small portions of copyrighted works as part of a dataset.

Finally, the Ninth Circuit may decide to do away with the identicality requirement altogether. While this may seem like a potential boon to plaintiffs, who could allege that removal of CMI and distribution of some copied material, no matter how small, plaintiffs would still face substantial challenges.  Elimination of the identicality requirement would likely lead to greater weight being placed on the knowledge requirement in courts’ assessments of §1202(b) claims, which requires that defendants know or have reasonable grounds to know that their actions will “induce, enable, facilitate, or conceal an infringement.” In the context of the Github case, even without an identicality requirement, plaintiffs §1202(b) claims contain scant factual allegations about the defendants’ CMI removal and knowledge in the court filings to date. For other developers and users of AI, the effects of not having an identicality requirement would likely vary on a case-by-case basis. 

Conclusion

Recent copyright infringement suits and the pending appeal to the Ninth Circuit in Doe 1 v. Github demonstrate that §1202(b) is having its day in the sun. Although the provision has been overlooked and infrequently litigated in the past, the scope of protections granted by §1202(b) is important for understanding whether and how AI developers can remove CMI when using copyrighted works to process, restructure, and analyze copyrighted works for AI development. Thus, as lawsuits against AI developers and users continue to progress, the requirements to have a valid §1202(b) claim are sure to become even more contentious.

Text Data Mining Research DMCA Exemption Renewed and Expanded

Posted October 25, 2024
U.S. Copyright Office 1201 Rulemaking Process, taken from https://www.copyright.gov/1201/

Earlier today, the Library of Congress, following recommendations from the U.S. Copyright Office, released its final rule adopting exemptions to the Digital Millenium Copyright Act’s prohibition on circumvention of technological protection measures (e.g.,  DRM).  

As many of you know, we’ve been working closely with members of the text and data-mining community as well as our co-petitioners, the Library Copyright Alliance (LCA) and the American Association of University Professors (AAUP), to petition for renewal of the existing TDM research exemption and to expand it to allow researchers to share their research corpora with other researchers outside of their university (something not previously allowed). The process began over a year ago and followed an in-depth review process by the U.S. Copyright Office. 

We are very pleased to see that the Librarian of Congress both approved the renewal of the existing exemption and approved an expansion that allows for research universities to provide access to TDM corpora for use by researchers at other universities. 

The expanded rule is poised to make an immediate impact in helping the TDM researchers collaborate and build upon each other’s work. As Allison Cooper,  director of Kinolab and Associate Professor of Romance Languages and Literatures and Cinema Studies at Bowdoin College, explains:

“This decision will have an immediate impact on the ongoing close-up project that Joel Burges, Emily Sherwood, and I are working on by allowing us to collaborate with researchers like David Bamman, whose expertise in machine learning will be valuable in answering many of the ‘big picture’ questions about the close-up that have come up in our work so far.”

These are the main takeaways from the new rule: 

  • The exemption has been expanded to allow “access” to corpora by researchers at other institutions “solely for purposes of text and data mining research or teaching.” There is no more requirement that access be granted as part of a “collaboration,” so new researchers can ask new and different questions of a corpus. Access must be credentialed and authenticated.
  • The issue of whether a researcher can engage in “close viewing” of a copyrighted work has been resolved—as the explanation for the revised rule puts it, researchers can “view the contents of copyrighted works as part of their research, provided that any viewing that takes place is in furtherance of research objectives (e.g., processing or annotating works to prepare them for analysis) and not for the works’ expressive value.” This is a very helpful clarification!
  • The new rule also modified the existing security requirements, which provide that researchers must put in place adequate security protocols to protect TDM corpora from unauthorized reuse and must share information about those security protocols with rightsholders upon request. That rule has been limited in some ways and expanded in others. The new rule clarifies that trade associations can send inquiries on behalf of rightsholders. However, inquiries must be supported by a “reasonable belief” that the sender’s works are in a corpus being used for TDM research.

Later on, we will post a more in-depth analysis of the new rules–both TDM and others that apply to authors. The Librarian of Congress also authorized the renewal of a number of other rules that support research, teaching, and library preservation. Among them is a renewal of another exemption that Authors Alliance and AAUP petitioned for, allowing for the circumvention of digital locks when using motion picture excerpts in multi-media ebooks. 

Thank you to all of the many, many TDM researchers and librarians we’ve worked with over the last several years to help support this petition. 

You can learn more about TDM and our work on this issue through our TDM resources page, here.

Who Represents You in the AI Copyright Lawsuits? 

Posted October 16, 2024

Sara Silverman is the author of The Bedwetter, a comedy memoir.  Richard Kadrey wrote Sandman Slim, a fantasy novel series. Christopher Golden, a supernatural thriller titled Ararat. 

These authors might not seem to have much in common with an academic author who writes in history, physics, or chemistry. Or a journalist. Or a poet. Or, for that matter, me, writing this blog post.  And yet, these authors may end up representing us all in court. 

A large number of the recent AI copyright lawsuits are class action lawsuits. This means that these lawsuits are brought by a small number of plaintiffs who (subject to judicial approval) are granted the right to represent a much larger class. In many of the AI copyright lawsuits,  the proposed classes are extraordinarily broad, including many creators who might be surprised that they are being represented. If you live in the US and wrote something that was published online, there is a good chance that you are included in multiple of these classes. 

A very brief background on class action lawsuits

Class actions can be an efficient way of resolving disputes that involve lots of people, allowing for a single resolution that binds many parties when there are common interests and facts. As you can imagine, the class action mechanism can also attract misuse, for example, by plaintiffs (and their attorneys) who may seek large settlements on behalf of a large number of people. Those settlements may benefit the named plaintiffs and their attorneys but they aren’t really aligned with the interests of most class members. 

There are rules in place to prevent that kind of abuse.  In federal courts (where all copyright lawsuits must be brought), Rule 23 of the Federal Rule of Civil Procedure governs. It provides that:

“One or more members of a class may sue or be sued as representative parties on behalf of all members only if: 
(1) the class is so numerous that joinder of all members is impracticable; [“numerosity”]
(2) there are questions of law or fact common to the class; [“commonality”]
(3) the claims or defenses of the representative parties are typical of the claims or defenses of the class; [“typicality”] and
(4) the representative parties will fairly and adequately protect the interests of the class. [“adequacy”]”

The rest of Rule 23 contains a number of other safeguards to protect both class members and defendants. Among them are requirements that the court must certify that the class complies with rule 23,  that any proposed settlements be approved by the court,  and that class members receive notice of any proposed settlement and an opportunity to object. Additionally, there are a number of rules to ensure that the law firm bringing the suit can fairly and competently represent the class members. 

Class definition and class representatives in the copyright AI lawsuits

We believe it’s important for creators to pay attention to these suits because if a class is certified and that class includes those creators, the class representatives will have meaningful legal authority to speak on their behalf.  

Rule 23  provides that “at an early practicable time after a person sues,” the court must decide whether to certify the proposed class. Though we are now well over a year into some of the earliest suits filed, this has yet to happen. In the meantime what we have are proposed class definitions offered by plaintiffs. How broadly or narrowly a class is defined by the plaintiffs will be one of the most important factors in whether the class can be certified since it will directly affect the commonality of facts among the class, the typicality of claims, and whether the representatives can fairly and adequately represent the interests of the class. Plaintiffs have the burden of proving that they have satisfied Rule 23. 

In these AI lawsuits, we see some themes in terms of class representative and proposed classes, with many offering very broad class definitions. For example, in the now-consolidated In re OpenAI ChatGPT Litigation, the class representatives are 11 fiction writers of books such as The Cabin at the End of the World, The Brief Wondrous Life of Oscar Wao, What the Dead Know and others. 

They propose to represent a class defined as follows:  

“All persons or entities domiciled in the United States that own a United States copyright in any work that was used as training data for the OpenAI Language Models during the Class Period [defined as June 28, 2020 to the present].” 

This kind of broad “anyone with a copyright in a work used for training” approach to class definition is repeated in a few other suits. For example, the consolidated Kadrey v. Meta lawsuit has a similar (and overlapping) grouping of fiction author class representatives and an almost identical proposed class definition. Dubus v. NVIDIA is another suit that takes essentially the same approach. 

Other AI lawsuits have more variation in class representatives. Huckabee v. Bloomberg, for example, is another suit with a similar class definition (basically, all copyrighted works owned by someone in the US and used for training Bloomberg’s LLM) but with class representatives that are a bit different: mostly authors of religious books and of course, Mike Huckabee, a politician. 

There is at least one class action that is more precise both in terms of proposed class representatives and their relation to the proposed class definition. The now-consolidated Authors Guild v. OpenAI suit has some 28 proposed class representatives, most of whom are authors of best-selling fiction and non-fiction trade books, 14 of whom are members of the Authors Guild. In this suit, the plaintiffs propose two classes: one for fiction authors and one for non-fiction authors. It also places some restrictions around them: class members for fiction works must be “natural persons” who are “sole authors of, and legal or beneficial owners of Eligible Copyrights in” fictional works that were registered with the U.S. Copyright Office and used for training the defendants’ LLMs (and this includes persons who are beneficiaries of works held by literary estates). For nonfiction authors, class members are “[a]ll natural persons, literary trusts, and literary estates in the United States who are legal or beneficial owners of Eligible Nonfiction Copyrights’ which the complaint defines as works used to train defendants’ LLMs and that have an ISBN with the exception of any books classified as reference works (BISAC code REF). 

Some challenges and dangers
When you consider the scale and scope of materials used to train the AI models in question, you can immediately see some of the challenges that are likely to arise with relatively small groups of authors attempting to represent practically all individual U.S. copyright owners. 

While the exact training materials used for the models at issue remain opaque, it’s definitely true that they were not just trained on modern fiction. There is widespread acknowledgment that these models are trained on a large amount of content scraped from across the internet using data sources such as Common Crawl. This, in effect, means that these suits implicate the rights of millions of rights holders, with interests as diverse as those of YouTube content creators, computer programmers, novelists, academics, and more. 

How can these representatives fairly and adequately represent such a broad and diverse group–especially when many may disagree with the underlying motivations for the suit to begin with–is a tough question. Even the Authors Guild consolidated case, which is much more careful in terms of class definition, includes classes that are breathtakingly broad when one considers the diversity of authorship within them. The fiction author class, for example, could include everyone from NY Times bestselling authors to fan fiction writers. The nonfiction class, which is at least limited to nonfiction book authors of works assigned an ISBN, could similarly include everyone from authors of popular self-help books distributed by the millions to scholarly books with print runs in the low hundreds and distributed online on open-access terms. The interests, financial and otherwise, of those authors can vary significantly. 

Beyond the adequacy of representatives (along with questions about whether their experiences are really typical of others in the proposed class), there are other challenges unique to copyright law, for example, the opaque nature of ownership (there is no official public record of who owns what), making ascertaining who actually falls within the class an initial challenge. Compounding that, there are a dizzying variety of unique terms under which works are distributed online, some of which may afford AI developers a viable defense for many works. A fair use defense also requires some level of assessment of the nature of the works used, a fact-intensive inquiry that will vary from one work to another. This just scratches the surface of some of the issues that likely mean there really aren’t common questions of law or fact among the class. 

Conclusion
There are good reasons to think that the classes as currently defined in these lawsuits are too broad. For some of the reasons mentioned above, I think it will be difficult for courts to certify them as is. But this doesn’t mean authors and other rightsholders should sit back and assume that their interests won’t be co-opted by others in these suits who seek to represent them. We don’t know when the courts will actually address these class certification issues in these suits. When they do, it will be important for authors to speak up. 

Artist Left with Heavy Fees by Copyright Troll Law Firm

Posted October 11, 2024

Facts of the Case & Fair Use

On September 18, the 5th Circuit decided in Keck v. Mix Creative Learning Center that using copyrighted artwork to teach children how to make art in a similar style does not constitute copyright infringement. The case adds to the well-developed jurisprudence that teaching with copyrighted materials is often protected by fair use.

This case was initially filed in 2021 by plaintiff’s counsel, Mathew Kidman Higbee, a known and prolific copyright litigation firm sometimes accused of troll-like behavior.  During the pandemic, the defendant sold a total of six art kits (out of the six kits sold, two were purchased by the plaintiff) that included images of the plaintiff’s dog-themed artworks, biographical information, and details on her artistic styles. Additionally, the kit included paint, paintbrushes, and collage paper. The plaintiff’s side argued that including the artworks in teaching kits constituted willful copyright infringement and therefore demanded $900,000 in damages—to make up for the $250 the defendant made in sales. 

The district court dismissed all infringement claims in 2022; and last month, the 5th Circuit court affirmed that including copies of plaintiff’s artwork in a teaching kit is fair use. 

The courts found the first and fourth fair use factors to favor the defendant. Under the first factor, even though the defendant’s use was commercial in nature, by accompanying the artworks with art theory and history, the teaching kit transformed the original decorative purpose of the dog-themed artworks. The 5th Circuit distinguished this case from Warhol by pointing out that, in the Warhol case, the infringing use served the same illustrative purpose as the original work, while in this case, “the art kits had educational objectives, while the original works had aesthetic or decorative objectives.”  

Under the fourth factor, courts explained that they cannot imagine how the market value of plaintiff’s dog-themed artworks could decrease when included in children’s art lesson kits. The 5th Circuit Court further pointed out that there was no evidence that a market for licensing artworks for similar teaching kits exists now or is ever likely to develop. 

Because these “two most important” factors favored the defendant, the defendant’s use was fair use.

Fee Shifting: Plaintiffs Beware of Copyright Troll Law Firms!

The final outcome of the case: the plaintiff was ordered to cover $102,404 in fees and $165.72 in costs for the defendant.

Even though we are happy for the defendant and her counsel that, after a prolonged legal battle, this well-deserved victory is finally won, it is nevertheless disheartening to see the plaintiff-artist left alone in the end to face the high legal fees of this ill-conceived lawsuit. The plaintiff’s counsel not only failed to advise the plaintiff to act in her own best interest (whether it is to settle the case at the right moment or to pursue more plausible claims), but also conjured up willful infringement claims that were clearly meritless to any trained eye. Even the 5th Circuit Court lamented over this in its opinion, as it begrudgingly upheld the district court’s decision based on the abuse of discretion standard it must follow:

It is troubling that Keck alone will be liable for the high fees incurred by Defendants largely because of Higbee & Associates’ overly aggressive litigation strategy. From our review of the record, the law firm lacked a firm evidentiary basis to pursue hundreds of thousands of dollars in statutory damages against Defendants for willful infringement. Nevertheless, we cannot say, on an abuse of discretion standard, that the district court erred by determining that there was insufficient evidence that the firm’s conduct was both unreasonable and vexatious. … But we warn Higbee & Associates that future conduct of this nature may well warrant sanctions, and nothing in this opinion prevents Higbee & Associates from compensating its client, if appropriate, for the fees that she is now obliged to pay Defendants.

This should serve as a cautionary tale for would-be plaintiffs: copyright lawsuits, like any other type of litigation, are primarily meant to address the damages plaintiffs actually suffered, and the final settlement should make plaintiffs whole again—that is, as if no infringement has ever occurred. Copyright lawsuits (or the threat to sue) should not be undertaken as a way to create brand new income streams, such as was the case in the lawsuit described above. 

When someone aggressively enforces dubious copyright claims with the sole purpose of collecting exorbitant fees rather than protecting any underlying copyrights, they are called a “copyright troll.” Regrettably, beyond the disreputable law firms that are enthused to pursue aggressive claims, many services now exist to tempt creators into troll-like behavior by promising “new licensing income.” The true aim of these services is solely to collect high representation charges from creators, when users of the creators’ works are harassed into paying exorbitant settlements. Many victims often agree to pay just for the nuisance to stop. This predatory business model has been repeatedly exposed by creators and authors, including famously by Cory Doctorow

Needless to say, copyright trolls are harmful to the copyright ecosystem. Obviously, innocent users are harmed when slapped with unreasonable demand letters or even frivolous lawsuits. Worse, creators are misled into supporting this unethical practice while deluded into believing they are doggedly following the spirit of the law—sometimes, as was in this case, they are left to face the inevitable consequences of bringing a frivolous lawsuit, while the lawyer or agent that originally led them into the mire gets off free, upward and onward to their next “representation.” 

It was very unfortunate that the district court did not fully study the plaintiff’s counsel’s track record and issue appropriate disciplinary orders against him. The problem of copyright trolls will have to be addressed soon in order to preserve a healthy copyright system. 

What is “Derivative Work” in the Digital Age?

Posted October 7, 2024
on the top, Seltzer v. Green Day; on the bottom, Kienitz v. Sconnie Nation

Part I: The Problem with “Derivative Work”

The right to prepare derivative works is one of the exclusive rights copyright holders have under §106 of the Copyright Act. Other copyright holders’ exclusive rights include the right to make and distribute copies, and to display or perform a work publicly. 

Lately, we’ve seen a congeries of novel conceptions about “derivative works.” For example, a reader of our blog stated that when looking at AI models and AI outputs, works should be considered infringing “derivatives” even when there is no substantial similarity between the infringing AI model/outputs and the ingested originals. Even in the courts, we’ve seen confusion, for example, Hachette v. Internet Archive presented us with the following statement about derivative works:

Changing the medium of a work is a derivative use rather than a transformative one. . . . In fact, we have characterized this exact use―“the recasting of a novel as an e-book”―as a “paradigmatic” example of a derivative work. [citation omitted; emphasis added]

These statements leave one to wonder—what is a copy, a derivative work, an infringing use, and a transformative fair use in the context of U.S. copyright law? In order to have some clarity on these questions, it’s helpful to juxtapose “derivative works” first with “copies” and then with “transformative uses.” We think the confusion about derivative work and its related concepts arises out of using the phrase to mean “a work that is substantially similar to the original work” as well as “a work that is so in an unauthorized way, not excused from liabilities.”

There are many immediate real world implications for confusion over the meaning of “derivative work.” In privately negotiated agreements, licensees who have a right to make reproductions but not derivative works may be confused as to what medium their use is restricted to. For example, a publisher of a book with a license that allows it to make reproductions but not derivatives might be confused as to whether, under the Hachette court’s reasoning, it is allowed to republish a print book in a digital format such as a simple PDF of a scan. Similarly, for public licenses, such as the CC ND licenses, where a licensor stipulates restriction on the creation of derivative works, it causes confusion for downstream users whether, say, changing a pdf into a Word document is allowed. 

This is also an important topic to explore both in the recent hot debates over Controlled Digital Lending and generative artificial intelligence, as well as in an author’s everyday work—for instance, would quoting someone else’s work make your article/book a derivative work of the original? 

Part II: “Copies” and “Derivatives”

Our basic understanding of derivative works comes from the 1976 Copyright Act. The §101 definition tells us:

A “derivative work” is a work based upon one or more preexisting works, such as a translation, musical arrangement, dramatization, fictionalization, motion picture version, sound recording, art reproduction, abridgment, condensation, or any other form in which a work may be recast, transformed, or adapted. A work consisting of editorial revisions, annotations, elaborations, or other modifications which, as a whole, represent an original work of authorship, is a “derivative work”.

The U.S. Copyright Office published Circular 14 gives some further helpful guidance as to what a §106 derivative work would look like:

To be copyrightable, a derivative work must incorporate some or all of a preexisting “work” and add new original copyrightable authorship to that work. The derivative work right is often referred to as the adaptation right. The following are examples of the many different types of derivative works: 

  • A motion picture based on a play or novel 
  • A translation of an novel written in English into another language
  • A revision of a previously published book 
  • A sculpture based on a drawing 
  • A drawing based on a photograph 
  • A lithograph based on a painting 
  • A drama about John Doe based on the letters and journal entries of John Doe 
  • A musical arrangement of a preexisting musical work 
  • A new version of an existing computer program 
  • An adaptation of a dramatic work 
  • A revision of a website

One immediate observation that can be made from reading these, is that “ebook” or “digitized version of a work” is not listed as, nor similar to any of the exemplary derivative works in the Copyright Act or the Copyright Office Circular. By contrast, “ebook” or “digitized version of a work” seems to fit much better under the § 101 definition of “copies”:

“Copies” are material objects, other than phonorecords, in which a work is fixed by any method now known or later developed, and from which the work can be perceived, reproduced, or otherwise communicated, either directly or with the aid of a machine or device. The term “copies” includes the material object, other than a phonorecord, in which the work is first fixed.

The most crucial difference between a “copy” and a “derivative work” is whether new authorship is added. If no new authorship is added, merely changing the material that the work is fixed on does not create a new copyrightable derivative work. This, in fact, is observed by many courts before Hachette. For example, in Corel v. Bridgeman Art Gallery, the court unequivocally held that there is no new copyright granted to photos of public domain paintings. 

Additionally, as we know from Feist v. Rural Tel., “[t]he mere fact that a work is copyrighted does not mean that every element of the work may be protected.” Copyright protection is only limited to the original elements of a work. We cannot call a work “derivative” of another if it does not incorporate any copyrightable elements from the original copyrighted work. For example, the “Game Genie” device, which let players change elements of a Nintendo game, was not found to be a derivative work by the court because it didn’t incorporate any part of the Nintendo game. 

It is clear from this examination that sometimes a later-created work is a copy, sometimes a derivative, and sometimes it may not implicate any of the exclusive rights of the original.

Part III: “Derivative” and “Transformative” Works

Let’s quickly recap the context in which courts are confusing “derivative” and “transformative” works—

A prima facie case of copyright infringement requires the copyright holder to prove (1) ownership of a valid copyright, and (2) inappropriate copying of original elements. We will not go into more details here, but essentially, the inappropriate copying prong requires plaintiffs to assert and prove defendant’s access to the plaintiff’s work as well as a level of similarity between the works in question that shows improper appropriation of the plaintiff’s work. If the similarity between the defendant’s work and protectable elements in the plaintiff’s work is minimal, then there is no infringement. As seen in the  “Game Genie” example above, courts can rely on substantial similarity analysis to determine whether a work is indeed a potentially-infringing copy or derivative of the plaintiff’s work.

Once the plaintiff establishes a prima facie infringement case—e.g., the defendant’s work is shown to be a derivative or a copy of the plaintiff’s registered work—the defendant may still nevertheless be free to make the use if the use falls outside the ambit of the copyright holder’s §106 rights, such as uses that are fair use. Whether a work is a derivative work under § 106 is no longer a relevant inquiry after establishing a prima facie case: this point is starkly obvious when looking at the many plausible defenses a defendant can raise (including fair use) where even the verbatim copying of a work is authorized by law. 

As the court stated in Authors Guild v. Hathitrust, “there are important limits to an author’s rights to control original and derivative works. One such limit is the doctrine of ‘fair use,’ which allows the public to draw upon copyrighted materials without the permission of the copyright holder in certain circumstances.” When a prima facie infringement case is already established, yet a court still discusses whether the defendant’s work is a “derivative work,” at a minimum, the court adds confusion by beyond the § 101  definition of a derivative work. 

In fact, a distinct new significance is being given to “derivative work” in recent years in the context of the “purpose and character” factor of fair use, specifically, when analyzing if a use has a transformative purpose. The shift in a word’s meaning or a concept is not per se unimaginable or objectionable. It is misguided to consider the copyright legal landscape static. As law professor Pamela Samuelson pointed out, before the mid-19th century, most courts did not even think copyright holders were entitled to demand compensation from others preparing derivative works. The 1976 Copyright Act finally codified copyright holders’ exclusive right to prepare derivative works. And, now, some rights holders want the courts to say there are categorical derivative uses that can never be considered fair use.

The Hachette court is among those that have unfortunately bought into this novel approach. The court seems not only to misconstrue the salient distinction between a ‘copy of a work’ and a ‘derivative work’, they appear to give heightened protections to works they now define as ‘derivative’. If this misconception becomes widespread, we will be living in a world where if a use is new-derivative, then it is never transformative (and, if it is not transformative, it is likely not fair). Ultimately, it is purely circular for a court to say that the reason for denying the fair use defense is that the use is derivative. When we buy into this setup of “derivative v.s. transformative,” it is difficult to ever say with confidence that a work is transformative, because at the same time we remember how a transformative use should often fit in the actual definition of derivative work under § 101, “derivative”—just like the Green Day rendition of the plaintiff’s art in Seltzer v. Green Day.  

Clearly, if we take “derivative work” at its true § 101 definition, out of all potentially infringing works, “transformative fair use” is not an absolute complement, but a possible subset, of derivative works. We know from Campbell v. Acuff-Rose that “transformativeness is a matter of degree, not a binary;” whereas no such sliding scale is plausible for derivative works. A work is either a derivative or it is not: there’s never a “somewhat derivative” work in copyright. All in all, it makes little sense to frame the issues as “transformative v.s. derivative work”—such discussions inevitably buy into the rhetorics of copyright expansionists. We have already warned the court in Warhol against the danger of speaking heedlessly about derivative works in the context of fair use. We must ensure that the “derivative v.s. transformative” dichotomy does not come to dominate future discussions of fair use, so that we conserve the utility and clarity of the fair use doctrine.

The expansion of the relevance of “derivative work” beyond the establishment of a prima facie infringement case not only creates a circular reasoning for denying fair use, but also makes it impossible to make sense of the case law we have accumulated on fair use. Take Seltzer v. Green Day for example, the court held that a work can be transformative even if that work “makes few physical changes to the original.” The Green Day concert background art with a red cross superimposed was found to be a fair use of the original street art—a classic example of how a prima facie infringing derivative work can nevertheless be a transformative, and thus fair, use. Similarly, in Kienitz v. Sconnie Nation, a derivative use of a photo on a tshirt was found to be a fair use. Ideas and concepts, including “derivative works,” are only important to the extent they elucidate our understanding of the world. When the use of “derivative works” leads to more confusion than clarity, we should be cautious in adopting the new meaning being superimposed on “derivative works.”

The AI Copyright Hype: Legal Claims That Didn’t Hold Up

Posted September 3, 2024

Over the past year, two dozen AI-related lawsuits and their myriad infringement claims have been winding their way through the court system. None have yet reached a jury trial. While we all anxiously await court rulings that can inform our future interaction with generative AI models, in the past few weeks, we are suddenly flooded by news reports with titles such as “US Artists Score Victory in Landmark AI Copyright Case,” “Artists Land a Win in Class Action Lawsuit Against A.I. Companies,” “Artists Score Major Win in Copyright Case Against AI Art Generators”—and the list goes on. The exuberant mood in these headlines mirror the enthusiasm of people actually involved in this particular case (Andersen v. Stability AI). The plaintiffs’ lawyer calls the court’s decision “a significant step forward for the case.” “We won BIG,” writes the plaintiff on X

In this blog post, we’ll explore the reality behind these headlines and statements. The “BIG” win in fact describes a portion of the plaintiffs’ claims surviving a pretrial motion to dismiss. If you are already familiar with the motion to dismiss per Federal Rules of Civil Procedure Rule 12(b)(6), please refer to Part II to find out what types of claims have been dismissed early on in the AI lawsuits. 

Part I: What is a motion to dismiss?

In the AI lawsuits filed over the last year, the majority of the plaintiffs’ claims have struggled to survive pretrial motions to dismiss. That may lead one to believe that claims made by plaintiffs are scrutinized harshly at this stage. But that is far from the truth. In fact, when looking at the broader legal landscape beyond the AI lawsuits, Rule 12(b)(6) motions are rarely successful.

In order to survive a Rule 12(b)(6) motion to dismiss filed by AI companies, plaintiffs in these lawsuits must make “plausible” claims in their complaint. At this stage, the court will assume that all of the factual allegations made by the plaintiffs are true and interpret everything in a way most favorable to plaintiffs. This allows the court to focus on the key legal questions without getting caught up in disputes about facts. When courts look at plaintiffs’ factual claims in the best possible light, if the defendant AI companies’ liability can plausibly be inferred based on facts stated by plaintiffs, then the claims will survive a motion to dismiss. Notably, the most important issues at the core of these AI lawsuits—namely, whether there has been direct copyright infringement and what may count as a fair use—are rarely decided at this stage, because these claims raise questions about facts as well as the law. 

On the other hand, if the AI companies will prevail as a matter of law even when the plaintiffs’ well-pleaded claims are taken as entirely true, then the plaintiffs’ claims will be dismissed by court. Merely stating that it is possible that the AI companies have done something unlawful, for instance, will not survive a motion to dismiss; there must be some reasonable expectation that evidence can be found later during discovery to support the plaintiffs’ claims. 

Procedurally, when a claim is dismissed, the court will often allow the plaintiffs to amend their complaint. That is exactly what happened with Andersen v. Stability AI (the case mentioned at the beginning of this blog post): the plaintiffs’ claims were first dismissed in October last year, and the court allowed the plaintiffs to amend their complaint to address the deficiencies in their allegations. The newly amended complaint contains infringement claims that survived new motions to dismiss, as well as other breach of contract, unjust enrichment, and DMCA claims that again were dismissed.

As you may have guessed, including something like the “motion to dismiss” in our court system can help save time and money, so parties don’t waste precious resources on meritless claims at trial. One judge dismissed a case against OpenAI earlier this year, stating that “the plaintiffs need to understand that they are in a court of law, not a town hall meeting.” The takeaway: plaintiffs need to bring claims that can plausibly entitle them to relief.

Part II: What claims are dismissed so far?

Most of the AI lawsuits are still at an early stage, and most of the court rulings we have seen so far are in response to the defendants’ motions to dismiss. From these rulings, we have learned which claims are viewed as meritless by courts. 

The removal of copyright management information (“CMI,” which includes information such as the title, the copyright holder, and other identifying information in a copyright notice) is a claim included in almost all plaintiffs’ complaints in the AI lawsuits, and this claim has failed to survive motions to dismiss without exception. DMCA Section 1202(b) restricts the intentional, unauthorized removal of CMI. Experts initially considered DMCA 1202(b) one of the biggest hurdles for non-licensed AI training. But courts so far have dismissed all DMCA 1202(b) claims, including in J. Doe 1 v. GitHub, Tremblay v. OpenAI, Andersen v. Stability AI, Kadrey v. Meta Platforms, and Silverman v. OpenAI. The plaintiffs’ DMCA Section 1202(b)(1) claims have failed because plaintiffs were not able to offer any evidence showing their CMI has been intentionally removed by the AI companies. For example, in Tremblay v. OpenAI and Silverman v. OpenAI, the courts held that the plaintiffs did not argue plausibly that OpenAI has intentionally removed CMI when ingesting plaintiffs’ works for training. Additionally, plaintiffs’ DMCA Section 1202(b)(3) have failed thus far because the plaintiffs’ claims did not fulfill the identicality requirement. For example, in J. Doe 1 v. GitHub, the court pointed out that Copilot’s output did not tend to represent verbatim copies of the original ingested code. We now see plaintiffs voluntarily dropping the DMCA claims in their amended complaints, such as in Leovy v Google (formerly J.L. vs Alphabet). 

Another claim that has been consistently dismissed by courts is that AI models are infringing derivative works of the training materials. The law defines a derivative work as “a work based upon one or more preexisting works, such as a translation, musical arrangement, … art reproduction, abridgment, condensation, or any other form in which a work may be recast, transformed, or adapted.” To most of us, the idea that the model itself (as opposed to, say, outputs generated by the model) can be considered a derivative work seems to be a stretch. The courts have so far agreed. On November 20, 2023, the court in Kadrey v. Meta Platforms said it is “nonsensical” to consider an AI model a derivative work of a book just because the book is used for training. 

Similarly, claims that all AI outputs should be automatically considered infringing derivative works have been dismissed by courts, because the claims cannot point to specific evidence that an instance of output is substantially similar to an ingested work. In Andersen v. Stability AI, plaintiffs tried to argue “that all elements of [] Anderson’s copyrighted works [] were copied wholesale as Training Images and therefore the Output Images are necessarily derivative;” the court dismissed the argument because—besides the fact that plaintiffs are unlikely able to show substantial similarity—“it is simply not plausible that every Training Image used to train Stable Diffusion was copyrighted [] or that all [] Output Images rely upon (theoretically) copyrighted Training Images and therefore all Output images are derivative images. … [The argument for dismissing these claims is strong] especially in light of plaintiffs’ admission that Output Images are unlikely to look like the Training Images.”

Several of these AI cases have raised claims of vicarious liability—that is, liability for the service provider based on the actions of others, such as users of the AI models. Because a vicarious infringement claim must be based on a showing of direct infringement, the vicarious infringement claims are also dismissed in Tremblay v. OpenAI and Silverman v. OpenAI, when plaintiffs cannot point to any infringing similarity between AI output and the ingested books.

Many plaintiffs have also raised a number of non-copyright, state law claims (such as negligence or unfair competition) that have largely been dismissed based on copyright preemption. Copyright preemption prevents duplicitous state law claims when those state law claims are based on an exercise of rights that are equivalent to those provided for under the federal Copyright Act. In Andersen v. Stability AI, for example, the court dismissed the plaintiffs’ unjust enrichment claim because the plaintiffs failed to add any new elements that would distinguish their claim based on California’s Unfair Competition Law or common law from rights under the Copyright Act.

It is interesting to note that many of the dismissed claims in different AI lawsuits closely mimic one another, such as in Kadrey v. Meta Platforms, Andersen v. Stability AI, Tremblay v. OpenAI, and Silverman v. OpenAI. It turns out that the similarities are no coincidence—all these lawsuits are filed by the same law firm. These mass-produced complaints not only contain overbroad claims that are prone to dismissal, they also have overbroad class designations. In the next blog post, we will delve deeper into the class action aspect of the AI lawsuits. 

Clickbait arguments in AI Lawsuits (will number 3 shock you?)

Posted August 15, 2024

Image generated by Canva

The booming AI industry has sparked heated debates over what AI developers are legally allowed to do. So far, we have learned from the US Copyright Office and courts that AI created works are not protectable, unless it is combined with human authorship. 

As we monitor two dozen ongoing lawsuits and regulatory efforts that address various aspects of AI’s legality, we see legitimate legal questions that must be resolved. However, we also see some prominent yet flawed arguments that have been used to enflame discussions, particularly by publisher-plaintiffs and their supporters. For now, let’s focus on some clickbait arguments that sound appealing but are fundamentally baseless. 

Will AI doom human authorship?

Based on current research, AI tools can actually help authors improve creativity, productivity, as well as the longevity of their career

When AI tools such as ChatGPT first appeared online, many leading authors and creators publicly endorsed it as a useful tool like any other tech innovation that came before it. At the same time, many others claimed that authors and creators of lesser caliber will be disproportionately disadvantaged by the advent of AI. 

This intuition-driven hypothesis, that AI will be the bane of average authors, has so far proved to be misguided.

We now know that AI tools can greatly help authors during the ideation stage, especially for less creative authors. According to a study published last month, AI tools had minimal impact on the output of highly creative authors, but were able to enhance the works of less imaginative authors. 

AI can also serve as a readily-accessible editor for authors. Research shows that AI enhances the quality of routine communications. Without AI-powered tools, a less-skilled person will often struggle with the cognitive burden of managing data, which limits both the quality and quantity of their potential output. AI helps level the playing field by handling data-intensive tasks, allowing writers to focus more on making creative and other crucial decisions about their works. 

It is true that entirely AI-generated works of abysmal quality are available for purchase on some platforms. Some of these works are using human authors’ names without authorization. These AI-generated works may infringe on authors’ right of publicity, but they do not present commercially-viable alternatives to books authored by humans. Readers prefer higher-quality works produced with human supervision and interference (provided that digital platforms do not act recklessly towards their human authors despite generating huge profits from human authors).

Are lawsuits against AI companies brought with authors’ best interest in mind? 

In the ongoing debate over AI, publishers and copyright aggregators have suggested that they have brought these lawsuits to defend the interests of human authors. Consider the New York Times for example, in its complaint against OpenAI, NY Times describes their operations as “a creative and deeply human endeavor (¶31)” that necessitates “investment of human capital (¶196).” NY Times argues that OpenAI has built innovation on the stolen hard work and creative output from journalists, editors, photographers, data analysts, and others—an argument contrary to what the NY Times once argued in court in New York Times v. Tasini,  that authors’ rights must take a backseat to NY Times’ financial interests in new digital uses.  

It is also hard to believe that many of the publishers and aggregators are on the side of authors when we look at how they have approached licensing deals for AI training. These licensing deals can be extremely profitable for the publishers. For example, Taylor and Francis sold AI training data to OpenAI for 10 million USD. John Wiley and Sons earned $23 million from a similar deal with a non-disclosed tech company. Though we don’t have the details of these agreements, it seems easy to surmise that in return for the money received, the publishers will not harass the AI companies with future lawsuits. (See our previous blog post about these licensing deals and what you can do as an author.) It is ironic how an allegedly unethical and harmful practice quickly becomes acceptable once the publishers are profiting from it.

How much of the millions of dollars changing hands will go to individual authors? Limited data exist. We know that Cambridge University Press, a good-faith outlier, is offering authors 20% royalties if their work is licensed for AI training. Most publishers and aggregators are entirely opaque about how authors are to be compensated in these deals. Take the Copyright Clearance Center (CCC) for example, it offers zero information about how individual authors are consulted or compensated when their works are sold for AI training under CCC AI training license.

This is by no means a new problem for authors. We know that traditionally-published book authors receive around 10% of royalties from their publishers: a little under $2 per copy for most books. On an ebook, authors receive a similar amount for each “copy” sold. This little amount handed to authors only starts to look generous when compared to academic publishing, where authors increasingly pay publishers to have their articles published in journals. The journal authors receive zero royalties, despite the publishers’ growing profit

Even before the advent of AI technology, most authors were struggling to make a living on writing alone. According to an Authors Guild’s survey in 2018, the median income for full-time writers was $20,300, and for part-time writers, a mere $6,080. Fair wage and equitable profit sharing is an issue that needs to be settled between authors and publishers, even if publishers try to scapegoat AI companies. 

It’s worth acknowledging that it’s not just publishers and copyright industry organizations filing these lawsuits. Many of these ongoing lawsuits have been filed as class actions, with the plaintiffs claiming to represent a broad class of people who are similarly situated and (thus they alleged) hold similar views. Most notably, in Authors Guild v. OpenAI, Authors Guild and its named individual plaintiffs claim to represent all fiction writers in the US who have sold more than 5000 copies of a work. There’s also another case where plaintiff claims to represent all copyright holders of non-fiction works, including authors of academic journal articles, which got support from Authors Guild, and several others in which an individual plaintiff asserts the right to represent virtually all copyright holders of any type

As we (along with many others) have repeatedly pointed out, many authors disagree with the publishers and aggregators’ restrictive view on fair use in these cases, and don’t want or need a self-appointed guardian to “protect” their interests.  We have seen the same over-broad class designation in the Authors Guild v. Google case, which caused many authors to object, including many of our own 200 founding members.

Respect for copyright and human authors’ hard work means no more AI training under US copyright law? 

While we wait for courts to figure out the key questions on infringement and fair use, let’s take a moment to remember what copyright law does not regulate.

Copyright law in the US exists to further the Constitutional goal to “promote the Progress of Science and useful Arts.” In 1991, the Supreme Court held in Feist v. Rural Telephone Service that copyright cannot be granted solely based on how much time or energy authors have expended. “Compensation for hard work“ may be a valid ethical discussion, but it is not a relevant topic in the context of copyright law.

Publishers and aggregators preach that people must “respect copyright,” as if copyright is synonymous with the exclusive rights of the copyright holder. This is inaccurate and misleading. In order to safeguard the freedom of expression, copyright is designed to embody not only the rightsholders’ exclusive rights but also many exceptions and limitations to the rightsholders’ exclusive rights. Similarly, there’s no sound legal basis to claim that authors must have absolute control over their own work and its message. Knowledge and culture thrives because authors are permitted to build upon and reinterpret the works of others

Does this mean I should side with the AI companies in this debate?

Many of the largest AI companies exhibit troubling traits that they have in common with many publishers, copyright aggregators, digital platforms (e.g., Twitter, TikTok, Youtube, Amazon, Netflix, etc.), and many other companies with dominant market power. There’s no transparency or oversight afforded to the authors or the public. The authors and the public have little say in how the AI models are trained, just like how we have no influence over how content is moderated on digital platforms, how much royalties authors receive from the publishers, or how much publishers and copyright aggregators can charge users. None of these crucial systematic flaws will be fixed by granting publishers a share of AI companies’ revenue. 

Copyright also is not the entire story. As we’ve seen recently, there are some significant open questions about the right of publicity and somewhat related concerns about the ability of AI to churn out digital fakes for all sorts of purposes, some of which are innocent, but others are fraudulent, misleading, or exploitative. The US Copyright Office released a report on digital replicas on July 31 addressing the question of digital publicity rights, and on the same day the NO FAKES Act was officially introduced. Will the rights of authors and the public be adequately considered in that debate? Let’s remain vigilant as we wait to see the first-ever AI-generated public figure in a leading role to hit theaters in September 2024.