Category: Copyright

Chamber of Progress Says Tariffs Are an Excuse to Infringe Copyrights

tariff

Politico reported yesterday that the astroturf organization called Chamber of Progress stated that because Trump’s tariffs will be a “gut punch” to Silicon Valley stock prices, California legislators should decline to aggravate matters by passing a law that would require transparency among AI developers using copyrighted works in model training. Granted, the tone was more circumspect, but that’s what the argument boils down to:  Tariffs are going to screw our stock values, so we need to screw creators to offset the harm.

According to Chamber of Progress economist Kaitlyn Harger, the cost of compliance with AB 412, sponsored by Assembly Member Rebecca Bauer-Kahan, would cause a dip in stock values that “…could carve $381 million out of California’s tax haul from the four tech giants, all key players in the generative AI boom,” Politico reports.

I won’t comment on the numbers, especially because they are speculative, but I will note the amount of SOP fluff being used to package this argument against the transparency bill. Adam Eisgrau, senior director of AI, creativity, and copyright policy at Chamber of Progress states that founding this anti-AB 412 argument in the tariff controversy is “not opportunistic,” when of course it is. He states, “It is fair to call tariffs a tax, and I think it’s fair to call this bill an innovation tax.”

Kudos for dinging tariffs and taxes and promoting innovation in one sentence, but Eisgrau is parroting a longstanding practice of Silicon Valley, calling any price it would pay for necessary materials a “tax” on progress. While compliance with AB 412’s transparency provisions would naturally cost the tech giants something, why is that cost, let alone the effect of tariffs, a basis for ignoring the creators’ whose works are being mined for AI training?

Assuming tariffs will hit every sector and increase prices across multiple supply chains, that universal condition is not a rationale for tech giants getting a supply of copyrighted works for free. The creators who make those works aren’t getting their supplies for free—and most creators barely make a living wage if they’re lucky. Meanwhile, if the California Assembly is looking broadly at the state’s economy in this North v. South narrative, even a cursory review of the numbers shows that motion picture production supports more jobs than the tech giants.

“Bauer-Kahan’s proposal has the backing of Hollywood labor groups,” Politico states, “including the powerful actors’ guild SAG-AFTRA and the National Association of Voice Actors. But it’s been side-eyed by tech industry critics who say it would upend fair-use protections and turn AI training into a lawsuit in waiting.”

This “upend fair use” claim, whether it comes from Eisgrau or any other tech representative, is standard parlor trick of that industry. First, they advocate a broad, generalized application of fair use (a doctrine that defies generalization) and then claim that any counterargument to their position would “upend” some standard that has been established. This is simply false.

AI training with protected works presents a novel set of facts to be weighed in context to fair use case law, and, thus, a finding that training is not fair use would not “upend” precedent. On the other hand, the rhetoric used by Big Tech in this regard asks for a “fair use” application so sweeping that it would be tantamount to a statutory carve-out for all machine learning now or in the future. That is asking to upend fair use.

The consensus appears to be that Trump’s tariff tactics can only sow chaos and drive up the cost of living for all Americans—including, by the way, creators of works protected by copyright. But despite the prospect of universal economic pain, the Chamber of Progress asks California lawmakers to shield a few of the wealthiest corporations on Earth from the rights and financial interests of the creators whose works those companies are exploiting. Wow.

Photo by Beebright

Maybe Now, Copyright Critics Know What Censorship Looks Like

censorship

Twelve years ago, when I first engaged in copyright advocacy, I was surprised to discover how many critics argued that copyright rights conflict with the speech right. Initially, I thought this had to be a fringe, internet thing—a vibe cooked up in the adolescent blogosphere that no legal scholar or expert took seriously. It would seem obviously contradictory to believe that any creative professional opposes the speech right. But no. It became clear that the main theme underlying the anti-copyright agenda—from academia to “digital rights” organizations to Techdirt et al.—was the premise that copyright rights are a means of censorship that should be minimally tolerated, if they are tolerated at all.

To support this view, and especially with regard to enforcing copyright rights online, it was apparently necessary to vilify creators as elitist, greedy, lazy, and even untalented individuals who expected society to pay for their “hobby.” Artists are used to this kind of criticism, historically from ultra-conservative voices, but the allegedly “democratizing” promise of the internet convinced many traditional liberals, and liberal organizations, to parrot this same anti-creator rhetoric.

Those familiar pejoratives are being recycled today by AI developers claiming that their products are just too damn important to let elitist, greedy, lazy creators stand in the way of machine learning. But let’s pause the AI skirmish a moment and back up. Because we should not lose sight of the fact that the original premise—that copyright rights conflict with speech was 1) bullshit; and 2) dangerous bullshit.

I lost count of how many posts, blogs, articles, and academic papers I read and/or rebutted trying to claim that copyright enforcement was making information, criticism, or important new expression disappear. None of those claims have been borne out by evidence, but more insidious was the fact that those who advocated the copyright-is-censorship theme were obscuring what real censorship looks like and, worse, feeding the very mechanisms by which true censors might come to power.

And come to power they have. As the Trump administration and likeminded state officials attack a wide spectrum of both creative and informative speech, will the anti-copyright crowd acknowledge how ridiculous their claims were that authors and publishers were ever the censors? No they will not. Will they acknowledge that the rights of authors are among the constitutional rights being trampled in Trump’s stampede toward national illiteracy? No they will not. Because it ain’t the authors and publishers trying to “memory hole” history. And it was ridiculous to suggest that they ever were.

But worse than the absurd premise that creators’ rights were a meaningful tool of censorship is that the anti-copyright narrative was promoted with substantial funding by the same companies whose technologies were destined to be exploited by the civil rights-infringing kakistocracy that now holds power. This was not just foreseeable; it was almost inevitable. As cited in my last post about the book Careless People, Sarah Wynn-Williams’s description of various authoritarians, including Trump, using the Facebook algorithm to micro-target disinformation is as unsurprising as it is shocking. What the hell did anyone imagine was really financing these “free information” machines? Goofy memes and mash-up videos?

Every time Mark Zuckerberg rebutted the idea of content moderation by saying, “We don’t want to be the arbiters of speech,” he was masking the truth that Facebook would take anybody’s money and guide them to effectively aim any misinformation at any parties for any purpose. It didn’t matter if the narrative was Brexit, the CCP spying on its own citizens, rallying Buddhists into murderous rage in Myanmar, or amplifying every delusional, unconstitutional syllable in Trump’s slow insurrection against the United States. The mantra of yellow journalism was If it bleeds, it leads, but the mantra of social media is If it pays, it stays.

Not that the anti-copyright crowd would ever admit they had anything to do with the damage Trump is doing to the Republic, but at least they might now concede that their claims about copyright making “information disappear” were as unworthy of attention as they were unfounded in fact. As Justice Sandra Day O’Connor famously wrote in Harper and Row v. Nation Enterprises, “The Framers intended copyright itself to be the engine of free expression.” And so it has been. Meanwhile, the tech industry that opposes those rights has proven to be an engine of so many calamities the Framers dearly hoped Americans would avoid.

Photo by Treephwood

Shedding Light: Briefs Filed in Kadrey v. Meta

kadrey

The purpose of cultivating works of authorship is to shed light on human experience, and the foundational purpose of the fair use doctrine in copyright law is to shed light on works of authorship. From its 18th century, English roots to the U.S. Supreme Court’s 2023 decision in AWF v. Goldsmith, the primary rationale for fair use is to permit the unlicensed use of works in ways that critique or comment upon the works themselves. Harvesting millions books to train an LLM does not do this.

With the growth of digital technologies and copyright protection for highly utilitarian computer code, fair use doctrine expands somewhat to permit certain “non expressive” uses of works. But these uses allowed by the courts have still tended to provide information about the works used or have been held to advance purposes like software interoperability. Harvesting millions of books to train an LLM does not do this.

A pair of briefs filed in Kadrey v. Meta—one by Association of American Publishers (AAP), the other filed by a group of IP law professors—present compelling arguments against finding that Meta’s unlicensed copying of millions of books to train its generative AI product Llama is fair use. A common theme in both briefs exposes a core fallacy, and legal hypocrisy, common to AI developers in these cases—namely that they copy protected “expression,” but they don’t copy protected “expression.”

As we see in the shorthand of social media, the developers write their own dichotomy by simultaneously humanizing and dehumanizing their products. In one breath, they compare machine leaning (ML) to human learning but then drop the analogy when they seek to claim that the protected “expression” in the works used is not copied or stored by their mysterious and complex “training” models. The AAP brief argues that copying “expression” is central to training an LLM, and the professors’ brief shows why “learning like a human” is precisely why fair use does not exempt Meta from obtaining licenses.

Both AAP and the professors naturally present specific arguments as to why none of the fair use case law supports Meta’s defense, but I was intrigued by the ways in which both briefs argue from different perspectives that training Llama indeed exploits the “expressive content” of the books appropriated. In fact, if it could be shown that no protected expression is copied or stored, this would be an argument that no case for infringement exists. But considering the emphasis on fair use—and all similar cases will almost certainly turn on fair use—we can assume that this statement from AAP is correct:

Meta would have this Court believe that authors’ original expression is not preserved in or exploited by the model. But this is not so. The LLM algorithmically maps and stores authors’ original expression so it can be used to generate output—indeed, that is the very point of the training exercise.

Kadrey and all AI training lawsuits with similar facts presented will turn on fair use factors one and four. Under factor two (nature of the works used), the books in Kadrey, and the works in most other cases, are “expressive” rather than “factual” in nature, and therefore, this factor favors plaintiffs. Under factor three (amount of the work used), it is understood that whole works have been fed into the LLM models, and so, this factor also favors plaintiffs.

Under the first fair use factor (purpose of the use), the court considers 1) whether the use is transformative; and 2) whether the use is commercial. Here, Meta’s commercial purpose is undeniable, and the AAP brief soundly argues that there is nothing transformative about copying the word-for-word expression in textual works for a purpose that sheds no light on the works used. On the contrary, the intent of the LLM is to create a non-human, substitute “author,” a purpose for which there is indeed no judicial precedent.

Factor four considers potential market harm to the copyright owner(s) of the work(s) used, and factor four may be the keystone in the broader creators versus GAI battle. Meta, a trillion-dollar company run by executives whose credibility is in doubt, contends that it is not feasible to license the books they used to train Llama. In response, AAP presents substantial evidence of licensing agreements between copyright owners and several major AI developers, and it states that Meta abandoned negotiations with publishers and chose instead to harvest books from pirate repositories.

Further, AAP argues “from a policy perspective” that Meta’s accessing those pirate “libraries” of DRM-free books militates against finding fair use in contravention of Congress’s intent when it passed the Digital Millennium Copyright Act (DMCA) in 1998. “Congress sought to establish a robust digital marketplace by ensuring appropriate safeguards for works made available online, including copyright owners’ ability to rely on DRM protections in distributing electronic copies of their works.”

In this spirit, inherent to the history of the fair use doctrine is the notion of “fair dealing” or, put differently, general legality in the overall purpose and character of the use. “The compiler of the training data’s knowledge of the unlawful provenance of the source copies might well taint the ‘character’ of the defendant’s use,” writes Professor Jane Ginsburg in a paper examining the question of fair use of works for AI training.[1]

The Professors’ Brief

The brief filed by the IP professors also emphasizes that the protected “expression” in the works is copied and exploited without license, but it also rather deftly uses Meta’s own rhetoric to doom the fair use defense. In general, when the AI cheerleaders say that LLMs “learn the way humans do,” my instinct has been to sneer at this anthropomorphic sentiment. But by giving the “learning like humans” analogy weight, the professors’ brief demonstrates exactly why that claim is fatal to a defense that the developer’s purpose is fair use.

Noting that humans indeed use protected works for “learning” all the time, the professors make plain that this exact relationship between author and reader (the basis for copyright) does not exempt the human from obtaining works legally. Thus, by Meta’s own analogy, the “machines learn like humans” claim is both an affirmation that the “expression” is being exploited and proof that that there is nothing transformative about using works for “learning.”

Further, the professors have a bit of fun emphasizing that Meta et al. strain to make the machine leaning process sound as technically complex as possible to obscure the fact that only by copying “expression” could the LLM actually “learn” anything. Here, a tip of the hat is deserved for the brief’s description of a human being reading a book thus:

… many billions of photons hit the book’s surface; some of those billions reached a lens, which focused them onto a retina, which converted them into electronic signals, which then resulted in electronic and chemical changes in some portion of over 100 billion neurons with over 100 trillion connections, some of those changes being transitory, and others more permanent.

The technical description of human processing and learning is even more mysterious because not even expert specialists in neuroscience know how the brain works at the neuronal level.

Well done! If that needlessly technical description of human reading requires legal access to the book, then so does the far less complex process of machine learning for AI development. Moreover, even if Meta were the vanguard developer and there were no examples of licensing deals being made, there is no rationale anywhere in commerce that a necessary resource must be free because it is essential. Meta et al. need electricity, engineers, and probably a computer or two to develop Lllama, and not one of these resources is free. Yet, somehow the most essential resource—the work of millions of authors—should be free.

On that note, there has never been a more important time to protect the rights and economic value of authors who shed light on the world we inhabit. I remain more than skeptical that it will ever be desirable to create literary works without authors, musical works without composers, etc. And certainly, licensing deals alone do not address all the potential hazards of unethical or questionable uses of generative AI. How products like Llama are used will provoke discussions that are cultural as well as legal. But for the moment, fair training of all AI models is the only rule that is both ethical and consistent with copyright’s purpose.

[1] Prof. Ginsburg is not one of the professors in the brief cited for this post.

Photo by Busko