Tag: opinion

Generative AI: Perfect Tool for the Age of Deception

For many reasons, the new millennium might well be described as the Age of Deception. Cokato Copyright Attorney Tom James explains why generative-AI is a perfect fit for it.

Woman's face superimposed on dense computer code, Illustrating generative AI
Image by Gerd Altmann on Pixabay.

What is generative AI?

“AI,” of course, stands for artificial intelligence. Generative AI is a variety of it that can produce content such as text and images, seemingly of its own creation. I say “seemingly” because in reality these kinds of AI tools are not really independently creating these images and lines of text. Rather, they are “trained” to emulate existing works created by humans. Essentially, they are derivative work generation machines that enable the creation of derivative works based on potentially millions of human-created works.

AI has been around for decades. It wasn’t until 2014, however, that the technology began to be refined to the point that it could generate text, images, video and audio so similar to real people and their creations that it is difficult, if not impossible, for the average person to tell the difference.

Rapid advances in the technology in the past few years have yielded generative-AI tools that can write entire stories and articles, seemingly paint artistic images, and even generate what appear to be photographic images of people. As I explained in AI Legal Issues, AI holds great potential as a facilitator of deception. Deception is so ubiquitous now, in fact, that AI tools themselves are joining in.

AI “hallucinations” (aka lies)

In the AI field, a “hallucination” occurs when an AI tool (such as ChatGPT) generates a confident response that is not justified by the data on which it has been trained.

For example, I queried ChatGPT about whether a company owned equally by a husband and wife could qualify for the preferences the federal government sets aside for women-owned businesses. The chatbot responded with something along the lines of “Certainly” or “Absolutely,” explaining that the U.S. government is required to provide equal opportunities to all people without discriminating on the basis of sex, or something along those lines. When I cited the provision of federal law that contradicts what the chatbot had just asserted, it replied with an apology and something to the effect of “My bad.”

I also asked ChatGPT if any U.S. law imposes unequal obligations on male citizens. The chatbot cheerily reported back to me that no, no such laws exist. I then cited the provision of the United States Code that imposes an obligation to register for Selective Service only upon male citizens. The bot responded that while that is true, it is unimportant and irrelevant because there has not been a draft in a long time and there is not likely to be one anytime soon. I explained to the bot that this response was irrelevant. Young men can be, and are, denied the right to government employment and other civic rights and benefits if they fail to register, regardless of whether a draft is in place or not, and regardless of whether they are prosecuted criminally or not. At this point, ChatGPT announced that it would not be able to continue this conversation with me. In addition, it made up some excuse. I don’t remember what it was, but it was something like too many users were currently logged on.

These are all examples of AI hallucinations. If a human being were to say them, we would call them “lies.”

Generating lie after lie

AI tools regularly concoct lies. For example, when asked to generate a financial statement for a company, a popular AI tool falsely stated that the company’s revenue was some number it apparently had simply made up. According to Slate, in their article, “The Alarming Deceptions at the Heart of an Astounding New Chatbot,” users of large language models like ChatGPT have been complaining that these tools randomly insert falsehoods into the text they generate. Experts now consider frequent “hallucination” (aka lying) to be a major problem in chatbots.

ChatGPT has also generated fake case precedents, replete with plausible-sounding citations. This phenomenon made the news when Stephen Schwartz submitted six fake ChatGPT-generated case precedents in his brief to the federal district court for the Southern District of New York in Mata v. Avianca. Schwartz reported that ChatGPT continued to insist the fake cases were authentic even after their nonexistence was discovered. The judge proceeded to ban the submission of AI-generated filings that have not been reviewed by a human, saying that generative-AI tools

are prone to hallucinations and bias…. [T]hey make stuff up – even quotes and citations. Another issue is reliability or bias. While attorneys swear an oath to set aside their personal prejudices,… generative artificial intelligence is the product of programming devised by humans who did not have to swear such an oath. As such, these systems hold no allegiance to…the truth.

Judge Brantley Starr, Mandatory Certification Regarding Generative Artificial Intelligence.

Facilitating defamation

Section 230 of the Communications Decency Act generally shields Facebook, Google and other online services from liability for providing a platform for users to publish false and defamatory information about other people. That has been a real boon for people who like to destroy other people’s reputations by means of spreading lies and misinformation about them online. It can be difficult and expensive to sue an individual for defamation, particularly when the individual has taken steps to conceal and/or lie about his or her identity. Generative AI tools make the job of defaming people even simpler and easier.

More concerning than the malicious defamatory liars, however, are the many people who earnestly rely on AI as a research tool. In July, 2023, Mark Walters filed a lawsuit against OpenAI, claiming its ChatGPT tool provided false and defamatory misinformation about him to journalist Fred Riehl. I wrote about this lawsuit in Generative AI: The Top 12 Lawsuits. Shortly after this lawsuit was filed, a defamation lawsuit was filed against Microsoft, alleging that its AI tool, too, had generated defamatory lies about an individual. Generative-AI tools can generate false and defamation statements about individuals even if no one has any intention of defaming anyone or ruining another person’s reputation.

Facilitating false light invasion of privacy

Pot, meet kettle; kettle, pot

“False news is harmful to our community, it makes the world less informed, and it erodes trust. . . . At Meta, we’re working to fight the spread of false news.” Meta (nee Facebook) published that statement back in 2017.  Since then, it has engaged in what is arguably the most ambitious campaign in history to monitor and regulate the content of conversations among humans. Yet, it has also joined other mega-organizations Google and Microsoft in investing billions of dollars in what is the greatest boon to fake news in recorded history: generative-AI.

Toward a braver new world

It would be difficult to imagine a more efficient method of facilitating widespread lying and deception (not to mention false and hateful rhetoric) – and therefore propaganda – than generative-AI. Yet, these mega-organizations continue to sink more and more money into further development and deployment of these lie-generators.

I dread what the future holds in store for our children and theirs.

Visit my extensive Copyright FAQs page.

Let’s Stop Analogizing Human Creators to Machines

Of course, policy discussions usually begin with the existing framework, but in this instance, it can be a shaky starting place because generative AI presents some unique challenges—and not just for the practice of copyright law.

[Guest post by David Newhoff, author of The Illusion of More.] Here he weighs in on the human authorship requirement.

Just as it is folly to anthropomorphize computers and robots, it is also unhelpful to discuss the implications of generative AI in copyright law by analogizing machines to authors.[1] In 2019, I explored the idea that “machine learning” could be analogous to human reading if the human happens to have an eidetic memory. But this was a thought exercise, and in that post, I also imagined machine training that serves a computer science or research purpose—not necessarily generative AIs trained on protected works designed to produce works without authors.

In the present discussion, however, certain parties weighing in on AI and copyright seem to advocate policy that is premised on the language and principles of existing doctrine as applicable to the technological processes of both the input and output sides of the generative AI equation. Of course, policy discussions usually begin with the existing framework, but in this instance, it can be a shaky starting place because generative AI presents some unique challenges—and not just for the practice of copyright law.

We should be wary of analogizing machine functions to human activity for the simple reason that copyright law (indeed all law) has never been anything but anthropocentric. Although it is difficult to avoid speaking in terms of machines “learning” or “creating,” it is essential that we either constantly remind ourselves that these are weak, inaccurate metaphors, or that a new glossary is needed to describe what certain AIs may be doing in the world of creative production.

On the input (training) side of the equation, the moment someone says something like, “Humans learn to make art by looking at art, and generative AIs do the same thing,” the speaker should be directed to the break-out session on sci-fi and excused from any serious conversation about applicable copyright law. Likewise, on the output side, comparisons of AI to other technological developments—from the printing press to Photoshop—should be presumed irrelevant unless the AI at issue can plausibly be described as a tool of the author rather than the primary maker of a work of creative expression.

Copyright Office Guidance Highlights Some Key Difficulties

To emphasize the exceptional nature of this discussion, even experts are somewhat confused by both the doctrinal and administrative aspects in the new guidelines published by U.S. Copyright Office directing authors how to disclaim AI-generated material in a registration application. The confusion is hardly surprising because generative AI has prompted the Office to ask an unprecedented question—namely, How was this work made?

As noted in several posts, copyrightability has always been agnostic with regard to the creative process. Copyright rights attach to works that show a modicum of originality, and the Copyright Office does not generally ask what tools, methods, etc. the author used to make a work.[2] But this historic practice was then confronted by the now widely reported applications submitted by Stephen Thaler and Kris Kashtanova, both claiming copyright in visual works made with generative AI.

In both cases, the Copyright Office rejected registration applications for the visual works based on the longstanding, bright-line doctrine that copyright rights can only attach to works made by human beings. In Thaler’s case, the consideration is straightforward because the claimant affirmed that the image was produced entirely by a machine. Kashtanova, on the other hand, asserts more than de minimis authorship (i.e., using AI as a tool) to produce the visual works elements in a comic book.

Whether in response to Kashtanova—or certainly anticipating applications yet to come—the muddiness of the Office guidelines is an attempt to address the difficult question as to whether copyright attaches to a work that combines authorship and AI generation, and how to draw distinctions between the two. This is not only new territory for the Office as a doctrinal matter but is a potential mess as an administrative one.

The Copyright Office has never been tasked with separating the protectable expression attributable to a human from the unprotectable expression attributable to a machine. Even if it could be said that photography has always provoked this tension (a discussion on its own), the analysis has never been an issue for the Office when registering works, but only for the courts in resolving claims of infringement. In fact, Warhol v. Goldsmith, although a fair use case, is a prime example of how tricky it can be to separate the factual elements of a photograph from the expressive elements.

But now the Copyright Office is potentially tasked with a copyrightability question that, in practice, would ask both the author and the examiner to engage in a version of the idea/expression dichotomy analysis—first separating the machine generated material from the author’s material and then considering whether the author has a valid claim in the protectable expression.

This is not so easy to accomplish in a work that combines author and machine-made elements in a manner that may be subtly intertwined; it begs new questions about what the AI “contributed” to a given work; and the inquiry is further complicated by the variety of AI tools in the market or in development. Then, because neither the author/claimant nor the Office examiner is likely a copyright attorney (let alone a court), the inquiry is fraught with difficulty as an administrative process—and that’s if the author makes a good-faith effort to disclaim the AI-generated material in the first place.

Many independent authors are confused enough by the Limit of Claim in a registration application or the concept of “published” versus “unpublished.” Asking these same creators to delve into the metaphysics implied by the AI/Author distinction seems like a dubious enterprise, and one that is not likely to foster more faith in the copyright system than the average indie creator has right now.

Copyrightability Could Remain Blind But …

It is understandable that some creators (e.g., filmmakers using certain plug-ins) may be concerned that the Copyright Office has already taken too broad a view—connoting a per se rule that denies copyrightability for any work generated with any AI technology. This concern is a reminder that AI should not be discussed as a monolithic topic because not all AI enhanced products do the same thing. And again, this may imply a need for some new terms rather than the words we use to describe human activities.

In this light, one could follow a different line of reasoning and argue that the agnosticism of copyrightability vis-à-vis process has always implied a presumption of human authorship where other factors—from technological enhancements to dumb luck—invisibly contribute to the protectable expression. Relatedly, a photographer can add a filter or plug-in that changes the expressive qualities of her image, but doing so is considered part of the selection and arrangement aspect of her authorship and does not dilute the copyrightability of the image.

Some extraordinary visual work has already been produced by professional artists using AI to yield results that are too strikingly well-crafted to believe that the author has not exerted considerable influence over the final image. In this regard, then, perhaps the copyrightability question at the registration stage, no matter how sophisticated the “filter” becomes, should remain blind to process. The Copyright Office could continue to register works submitted by valid claimants without asking the novel How question.

But the more that works may be generated with little or no human spark, the more this agnostic, status-quo approach could unravel the foundation of copyright rights altogether. And it would not be the first time that major tech companies have sought to do exactly that. It is no surprise that an AI developer or a producer using AI would seek the financial benefits of copyright protection; but without a defensible presence of human expression in the work, the exclusive rights of copyright cannot vest in a person with the standing to defend those rights. Nowhere in U.S. law do non-humans have rights of any kind, and this foundational principle reminds us that although machine activity can be compared to human activity as an allegorical construct, this is too whimsical for a serious policy discussion.

Again, I highlight this tangle of administrative and doctrinal factors to emphasize the point that generative AI does not merely present new variations on old questions (e.g., photography), but raises novel questions that cannot easily be answered by analogies to the past. If the challenges presented by generative AI are to be resolved sensibly, and in a way that will serve independent creators, policymakers and thought leaders on copyright law should be skeptical of arguments that too earnestly attempt to transpose centuries of doctrine for human activity into principles applied to machine activity.

[1] I do not distinguish “human” authors, because there is no other kind.

[2] I say “generally” only because I cannot account for every conversation among claimants and examiners.

Why Machine Training AI with Protected Works is Not Fair Use

… if the underlying goal of copyright’s exclusive rights and the fair use exception is to promote new “authorship,” this is doctrinally fatal to the proposal that training AIs on volumes of protected works favors a finding of fair use.

Guest blogger David Newhoff lays out the argument against the claim that training AI systems with copyright-protected works is fair use. David is the author of Who Invented Oscar Wilde? The Photograph at the Center of Modern American Copyright (Potomac Books 2020) and is a copyright advocate/writer at The Illusion of More.

As most copyright watchers already know, two lawsuits were filed at the start of the new year against AI visual works companies. In the U.S., a class-action was filed by visual artists against DeviantArt, Midjourney, and Stability AI; and in the UK, Getty Images is suing Stability AI. Both cases allege infringing use of large volumes of protected works fed into the systems to “train” the algorithms. Regardless of how these two lawsuits might unfold, I want to address the broad defense, already being argued in the blogosphere, that training generative AIs with volumes of protected works is fair use. I don’t think so.

Copyright advocates, skeptics, and even outright antagonists generally agree that the fair use exception, correctly applied, supports the broad aim of copyright law to promote more creative work. In the language of the Constitution, copyright “promotes the progress of science,” but a more accurate, modern description would be that copyright promotes new “authorship” because we do not tend to describe literature, visual arts, music, etc. as “science.”

The fair use doctrine, codified in the federal statute in 1976, originated as judge-made law, and from the seminal Folsom v. Marsh to the contemporary Andy Warhol Foundation v. Goldsmith, the courts have restated, in one way or another, their responsibility to balance the first author’s exclusive rights with a follow-on author’s interest in creating new expression. And as a matter of general principle, it is held that the public benefits from this balancing act because the result is a more diverse market of creative and cultural works.

Fair use defenses are case-by-case considerations and while there may be specific instances in which an AI purpose may be fair use, there are no blanket exceptions. More broadly, though, if the underlying goal of copyright’s exclusive rights and the fair use exception is to promote new “authorship,” this is doctrinally fatal to the proposal that training AIs on volumes of protected works favors a finding of fair use. Even if a court holds that other limiting doctrines render this activity by certain defendants to be non-infringing, a fair use defense should be rejected at summary judgment—at least for the current state of the technology, in which the schematic encompassing AI machine, AI developer, and AI user does nothing to promote new “authorship” as a matter of law.

The definition of “author” in U.S. copyright law means “human author,” and there are no exceptions to this anywhere in our history. The mere existence of a work we might describe as “creative” is not evidence of an author/owner of that work unless there is a valid nexus between a human’s vision and the resulting work fixed in a tangible medium. If you find an anonymous work of art on the street, absent further research, it has no legal author who can assert a claim of copyright in the work that would hold up in any court. And this hypothetical emphasizes the point that the legal meaning of “author” is more rigorous than the philosophical view that art without humans is oxymoronic. (Although it is plausible to find authorship in a work that combines human creativity with AI, I address that subject below.)

As a matter of law, the AI machine itself is disqualified as an “author” full stop. And although the AI owner/developer and AI user/customer are presumably both human, neither is defensibly an “author” of the expressions output by the AI. At least with the current state of technologies making headlines, nowhere in the process—from training the AI, to developing the algorithm, to entering prompts into the system—is there an essential link between those contributions and the individual expressions output by the machine. Consequently, nothing about the process of ingesting protected works to develop these systems in the first place can plausibly claim to serve the purpose of promoting new “authorship.”

But What About the Google Books Case?

Indeed. In the fair use defenses AI developers will present, we should expect to see them lean substantially on the holding in Authors Guild v. Google Books—a decision which arguably exceeds the purpose of fair use to promote new authorship. The Second Circuit, while acknowledging that it was pushing the boundaries of fair use, found the Google Books tool to be “transformative” for its novel utility in presenting snippets of books; and because that utility necessitates scanning whole books into its database, a defendant AI developer will presumably want to make the comparison. But a fair use defense applied to training AIs with volumes of protected works should fail, even under the highly utilitarian holding in Google Books.

While people of good intent can debate the legal merits of that decision, the utility of the Google Books search engine does broadly serve the interest of new authorship with a useful research tool—one I have used many times myself. Google Books provides a new means by which one author may research the works of another author, and this is immediately distinguishable from the generative AI which may be trained to “write books” without authors. Thus, not only does the generative AI fail to promote authorship of the individual works output by the system, but it fails to promote authorship in general.

Although the technology is primitive for the moment, these AIs are expected to “learn” exponentially and grow in complexity such that AIs will presumably compete with or replace at least some human creators in various fields and disciplines. Thus, an enterprise which proposes to diminish the number of working authors, whether intentionally or unintentionally, should only be viewed as devastating to the purpose of copyright law, including the fair use exception.

AI proponents may argue that “democratizing” creativity (i.e., putting these tools in every hand) promotes authorship by making everyone an author. But aside from the cultural vacuum this illusion of more would create, the user prompting the AI has a high burden to prove authorship, and it would really depend on what he is contributing relative to the AI. As mentioned above, some AIs may evolve as tools such that the human in some way “collaborates” with the machine to produce a work of authorship. But this hypothetical points to the reason why fair use is a fact-specific, case-by-case consideration. AI Alpha, which autonomously creates, or creates mostly without human direction, should not benefit from the potential fair use defense of AI Beta, which produces a tool designed to aid, but not replace, human creativity.

Broadly Transformative? Don’t Even Go There

Returning to the constitutional purpose of copyright law to “promote science,” the argument has already been floated as a talking point that training AI systems with protected works promotes computer science in general and is, therefore, “transformative” under fair use factor one for this reason. But this argument should find no purchase in court. To the extent that one of these neural networks might eventually spawn revolutionary utility in medicine or finance etc., it would be unsuitable to ask a court to hold that such voyages of general discovery fit the purpose of copyright, to say nothing of the likelihood that the adventure strays inevitably into patent law. Even the most elastic fair use findings to date reject such a broad defense.

It may be shown that no work(s) output by a particular AI infringes (copies) any of the works that went into its training. It may also be determined that the corpus of works fed into an AI is so rapidly atomized into data that even fleeting “reproduction” is found not to exist, and, thus, the 106(1) right is not infringed. Those questions are going to be raised in court before long, and we shall see where they lead. But to presume fair use as a broad defense for AI “training” is existentially offensive to the purpose of copyright, and perhaps to law in general, because it asks the courts to vest rights in non-humans, which is itself anathema to caselaw in other areas.[1]

It is my oft-stated opinion that creative expression without humans is meaningless as a cultural enterprise, but it is a matter of law to say that copyright is meaningless without “authors” and that there is no such thing as non-human “authors.” For this reason, the argument that training AIs on protected works is inherently fair use should be denied with prejudice.

***** 
n.b.: The Copyright Office has issued New AI Copyright Guidance. 

[1] Cetaceans v. Bus, holding that animals do not have standing in court, was the basis for rejecting PETA’S complaint against photographer Slater for infringing the copyright rights of the monkey in the “Monkey Selfie” fiasco.