Month: February 2023

A Recent Entrance to Complexity

The United States Copyright Office recently reaffirmed its position that it will not register AI-generated content, because it is not created by a human. The rule is easy to state; the devil is in the details. Attorney Thomas James explains.

Last year, the United States Copyright Office issued a copyright registration to Kristina Kashtanova for the graphic novel, Zarya of the Dawn. A month later, the Copyright Office issued a notice of cancellation of the registration, along with a request for additional information.

The Copyright Office, consistent with judicial decisions, takes the position that copyright requires human authorship. The Office requested additional information regarding the creative process that resulted in the novel because parts of it were AI-generated. Kashtanova complied with the request for additional information.

This week, the Copyright Office responded with a letter explaining that the registration would be cancelled, but that a new, more limited one will be issued. The Office explained that its concern related to the author’s use of Midjourney, an AI-powered image generating tool, to generate images used in the work:

Because Midjourney starts with randomly generated noise that evolves into a final image, there is no guarantee that a particular prompt will generate any particular visual output”

U.S. Copyright Office letter

The Office concluded that the text the author wrote, as well as the author’s selection, coordination and arrangement of written and visual elements, are protected by copyright, and therefore may be registered. The images generated by Midjourney, however, would not be registered because they were “not the product of human authorship.” The new registration will cover only the text and editing components of the work, not the AI-generated images.

A Previous Entrance to Paradise

Early last year, the Copyright Office refused copyright registration for an AI-generated image. Steven Thaler had filed an application to register a copyright in an AI-generated image called “A Recent Entrance to Paradise.” He listed himself as the copyright owner. The Copyright Office denied registration on the grounds that the work lacked human authorship. Thaler filed a lawsuit in federal court seeking to overturn that determination. The lawsuit is still pending. It is currently at the summary judgment stage.

The core issue

The core issue, of course, is whether a person who uses AI to generate content such as text or artwork can claim copyright protection in the content so generated. Put another way, can a user who deploys artificial intelligence to generate a seemingly expressive work (such as artwork or a novel) claim authorship?

This question is not as simple as it may seem. There can be different levels of human involvement in the use of an AI content generating mechanism. At one extreme, there are programs like “Paint,” in which users provide a great deal of input. These kinds of programs may be analogized to paintbrushes, pens and other tools that artists traditionally have used to express their ideas on paper or canvas. Word processing programs are also in this category. It is easy to conclude that the users of these kinds of programs are the authors of works that may be sufficiently creative and original to receive copyright protection.

At the other end of the spectrum are AI services like DALL-E and ChatGPT. Text and images can be generated by these systems with minimal human input. If the only human input is a user’s directive to “Write a story” or “Draw a picture,” then it would be difficult to claim that the author contributed any creative expression. That is to say, it would be difficult to claim that the user authored anything.

Peering into the worm can

The complicating consideration with content-generative AI mechanisms is that they have the potential to allow many different levels of user involvement in the generation of output. The more details a user adds to the instructions s/he gives to the machine, the more it begins to appear that the user is, in fact, contributing something creative to the project.

Is a prompt to “Write a story about a dog” a sufficiently creative contribution to the resulting output to qualify the user as an “author”? Maybe not. But what about, “Write a story about a dog who joins a traveling circus”? Or “Write a story about a dog named Pablo who joins a traveling circus”? Or “Write a story about a dog with a peculiar bark that begins, ‘Once upon a time, there was a dog named Pablo who joined a circus,’ and ends with Pablo deciding to return home”?

At what point along the spectrum of user-provided detail does copyright protectable authorship come into existence?

A question that is just as important to ask is: How much, if at all, should the Copyright Office involve itself with ascertaining the details of the creative process that were involved in a work?

In a similar vein, should copyright registration applicants be required to disclose whether their works contain AI-generated content? Should they be required to affirmatively disclaim rights in elements of AI-generated content that are not protected by copyright?

Expanding the Rule of Doubt

Alternatively, should the U.S. Copyright Office adopt something like a Rule of Doubt when copyright is claimed in AI-generated content? The Rule of Doubt, in its current form, is the rule that the U.S. Copyright Office will accept a copyright registration of a claim containing software object code, even though the Copyright Office is unable to verify whether the object code contains copyrightable work. If effect, if the applicant attests that the code is copyrightable, then the Copyright Office will assume that it is and will register the claim. Under 37 C.F.R. § 202.20(c)(2)(vii)(B), this may be done when an applicant seeks to register a copyright in object code rather than source code. The same is true of material that is redacted to protect a trade secret.

When the Office issues a registration under the Rule of Doubt, it adds an annotation to the certificate and to the public record indicating that the copyright was registered under the Rule of Doubt.

Under the existing rule, the applicant must file a declaration stating that material for which registration is sought does, in fact, contain original authorship.

This approach allows registration but leaves it to courts (not the Copyright Office) to decide on a case-by-case basis whether material for which copyright is claimed contains copyrightable authorship.  

Expanding the Rule of Doubt to apply to material generated at least in part by AI might not be the most satisfying solution for AI users, but it is one that could result in fewer snags and delays in the registration process.

Conclusion

The Copyright Office has said that it soon will be developing registration guidance for works created in part using material generated by artificial intelligence technology. Public notices and events relating to this topic may be expected in the coming months.

Need help with a copyright matter? Contact attorney Thomas James.

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.

[1] Cetaceans v. Bush 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.

A Thousand Cuts: AI and Self-Destruction

David Newhoff comments on generative AI (artificial intelligence) and public policy.

A guest post written by David Newhoff. AI, of course, stands for “artificial intelligence.” David is the author of Who Invented Oscar Wilde? The Photograph at the Center of Modern American Copyright (Potomac Books 2020) and a copyright advocate/writer at The Illusion of More.

I woke up the other day thinking about artificial intelligence (AI) in context to the Cold War and the nuclear arms race, and curiously enough, the next two articles I read about AI made arms race references. Where my pre-caffeinated mind had gone was back to the early 1980s when, as teenagers, we often asked that futile question as to why any nation needed to stockpile nuclear weapons in quantities that could destroy the world many times over.

Every generation of adolescents believes—and at times confirms—that the adults have no idea what the hell they’re doing; and watching the MADness of what often seemed like a rapturous embrace of nuclear annihilation was, perhaps, the unifying existential threat which shaped our generation’s world view. Since then, reasonable arguments have been made that nuclear stalemate has yielded an unprecedented period of relative global peace, but the underlying question remains:  Are we powerless to stop the development of new modes of self-destruction?

Of course, push-button extinction is easy to imagine and, in a way, easy to ignore. If something were to go terribly wrong, and the missiles fly, it’s game over in a matter of minutes with no timeouts left. So, it is possible to “stop worrying” if not quite “love the bomb” (h/t Strangelove); but today’s technological threats preface outcomes that are less merciful than swift obliteration. Instead, they offer a slow and seemingly inexorable decline toward the dystopias of science fiction—a future in which we are not wiped out in a flash but instead “amused to death” (h/t Postman) as we relinquish humanity itself to the exigencies of technologies that serve little or no purpose.

The first essay I read about AI, written by Anja Kaspersen and Wendell Wallach for the Carnegie Council, advocates a “reset” in ethical thinking about AI, arguing that giant technology investments are once again building systems with little consideration for their potential effect on people. “In the current AI discourse we perceive a widespread failure to appreciate why it is so important to champion human dignity. There is risk of creating a world in which meaning and value are stripped from human life,” the authors write. Later, they quote Robert Oppenheimer …

It is not possible to be a scientist unless you believe that the knowledge of the world, and the power which this gives, is a thing which is of intrinsic value to humanity, and that you are using it to help in the spread of knowledge, and are willing to take the consequences.

I have argued repeatedly that generative AI “art” is devoid of meaning and value and that the question posed by these technologies is not merely how they might influence copyright law, but whether they should exist at all. It may seem farfetched to contemplate banning or regulating the development of AI tech, but it should not be viewed as an outlandish proposal. If certain AI developments have the capacity to dramatically alter human existence—perhaps even erode what it means to be human—why is this any less a subject of public policy than regulating a nuclear power plant or food safety?

Of course, public policy means legislators, and it is quixotic to believe that any Congress, let alone the current one, could sensibly address AI before the industry causes havoc. At best, the tech would flood the market long before the most sincere, bipartisan efforts of lawmakers could grasp the issues; and at worst, far too many politicians have shown that they would sooner exploit these technologies for their own gain than they would seek to regulate it in the public interest. “AI applications are increasingly being developed to track and manipulate humans, whether for commercial, political, or military purposes, by all means available—including deception,” write Kaspersen and Wallach. I think it’s fair to read that as Cambridge Analytica 2.0 and to recognize that the parties who used the Beta version are still around—and many have offices on Capitol Hill.

Kaspersen and Wallach predict that we may soon discover that generative AI will have the same effect on education that “social media has had on truth.” In response, I would ask the following: In the seven years since the destructive power of social media became headline news, have those revelations significantly changed the conversation, let alone muted the cyber-libertarian dogma of the platform owners? I suspect that AI in the classroom threatens to exacerbate rather than parallel the damage done by social media to truth (i.e., reason). If social media has dulled Socratic skills with the flavors of narcissism, ChatGPT promises a future that does not remember what Socratic skills used to mean.

And that brings me to the next article I read in which Chris Gillard and Pete Rorabaugh, writing for Slate, use “arms race” as a metaphor to criticize technological responses to the prospect of students cheating with AI systems like ChatGPT. Their article begins:

In the classroom of the future—if there still are any—it’s easy to imagine the endpoint of an arms race: an artificial intelligence that generates the day’s lessons and prompts, a student-deployed A.I. that will surreptitiously do the assignment, and finally, a third-party A.I. that will determine if any of the pupils actually did the work with their own fingers and brain. Loop complete; no humans needed. If you were to take all the hype about ChatGPT at face value, this might feel inevitable. It’s not.

In what I feared might be another tech-apologist piece labeling concern about AI a “moral panic,” Gillard and Rorabaugh make the opposite point. Their criticism of software solutions to mitigate student cheating is that it is small thinking which erroneously accepts as a fait accompli that these AI systems are here to stay whether we like it or not. “Telling us that resistance to a particular technology is futile is a favorite talking point for technologists who release systems with few if any guardrails out into the world and then put the onus on society to address most of the problems that arise,” they write.

In other words, here we go again. The ethical, and perhaps legal, challenges posed by AI are an extension of the same conversation we generally failed to have about social media and its cheery promises to be an engine of democracy. “It’s a failure of imagination to think that we must learn to live with an A.I. writing tool just because it was built,” Gillard and Rorabaugh argue. I would like to agree but am skeptical that the imagination required to reject certain technologies exists outside the rooms where ethicists gather. And this is why I wake up thinking about AI in context to the Cold War, except of course that the doctrine of Mutually Assured Destruction was rational by contrast.

Photo by the author.

View the original article on The Illusion of More.

Contact attorney Tom James for copyright help

Need help registering a copyright or a group of copyrights in the United States, or enforcing a copyright in the United States? Contact attorney Tom James.

Getty Images Litigation Update

Getty Images has now filed a lawsuit for copyright infringement in the United States.

In a previous post, I reported on a lawsuit that Getty Images had filed in the United Kingdom against Stability AI. Now the company has filed similar claims against the company in the United States.

The complaint, which has been filed in federal district court in Delaware, alleges claims of copyright infringement; providing false copyright management information; removal or alteration of copyright management information; trademark infringement; trademark dilution; unfair competition; and deceptive trade practices. Both monetary damages and injunctive relief are being sought.

An interesting twist in the Getty litigation is that AI-generated works allegedly have included the Getty Images trademark.

Getty Images logo on AI-generated image
(Reproduction of a portion of the Complaint filed in Getty Images v. Stability AI, Inc. in U.S. district court for the district of Delaware, case no. Case 1:23-cv-00135-UNA (2023). The image has been cropped to avoid reproducing the likenesses of persons appearing in the image and to display only what is needed here for purposes of news reporting and commentary,)

Getty Images, which is in the business of collecting and licensing quality images, alleges (among other things) that affixing its trademark to poor quality AI-generated images tarnishes the company’s reputation. If proven, this could constitute trademark dilution, which is prohibited by the Lanham Act.

AI Legal Issues

Thomas James (“The Cokato Copyright Attorney”) describes the range of legal issues, most of which have not yet been resolved, that artificial intelligence (AI) systems have spawned.

AI is not new. Its implementation also is not new. In fact, consumers regularly interact with AI-powered systems every day. Online help systems often use AI to provide quick answers to questions that customers routinely ask. Sometimes these are designed to give a user the impression that s/he is communicating with a person.

AI systems also perform discrete functions such as analyzing a credit report and rendering a decision on a loan or credit card application, or screening employment applications.

Many other uses have been found for AI and new ones are being developed all the time. AI has been trained not just to perform customer service tasks, but also to perform analytics and diagnostic tests; to repair products; to update software; to drive cars; and even to write articles and create images and videos. These developments may be helping to streamline tasks and improve productivity, but they have also generated a range of new legal issues.

Tort liability

While there are many different kinds of tort claims, the elements of tort claims are basically the same: (1) The person sought to be held liable for damages or ordered to comply with a court order must have owed a duty to the person who is seeking the legal remedy; (2) the person breached that duty; (3) the person seeking the legal remedy experienced harm, i.e., real or threatened injury; and (4) the breach was the actual and proximate cause of the harm.

The kind of harm that must be demonstrated varies depending on the kind of tort claim. For example, a claim of negligent driving might involve bodily injury, while a claim of defamation might involve injury to reputation. For some kinds of tort claims, the harm might involve financial or economic injury. 

The duty may be specified in a statute or contract, or it might be judge-made (“common law.”) It may take the form of an affirmative obligation (such as a doctor’s obligation to provide a requisite level of care to a patient), or it may take a negative form, such as the common law duty to refrain from assaulting another person.

The advent of AI does not really require any change in these basic principles, but they can be more difficult to apply to scenarios that involve the use of an AI system.

Example. Acme Co. manufactures and markets Auto-Doc, a machine that diagnoses and repairs car problems. Mike’s Repair Shop lays off its automotive technician employees and replaces them with one of these machines. Suzie Consumer brings her VW Jetta to Mikes Repair Shop for service because she has been hearing a sound that she describes as being a grinding noise that she thinks is coming from either the engine or the glove compartment. The Auto-Doc machine adds engine oil, replaces belts, and removes the contents of the glove compartment. Later that day, Suzie’s brakes fail and her vehicle hits and kills a pedestrian in a crosswalk. A forensic investigation reveals that her brakes failed because they were badly worn. Who should be held liable for the pedestrian’s death – Suzie, Mike’s, Acme Co., some combination of two of them, all of them, or none of them?

The allocation of responsibility will depend, in part, on the degree of autonomy the AI machine possesses. Of course, if it can be shown that Suzie knew or should have known that her brakes were bad, then she most likely could be held responsible for causing the pedestrian’s death. But what about the others? Their liability, or share of liability, is affected by the degree of autonomy the AI machine possesses. If it is completely autonomous, then Acme might be held responsible for failing to program the machine in such a way that it would test for and detect worn brake pads even if a customer expresses an erroneous belief that the sound is coming from the engine or the glove compartment. On the other hand, if the machine is designed only to offer suggestions of possible problems and solutions,  leaving it up to a mechanic to accept or reject them, then Mike’s might be held responsible for negligently accepting the machine’s recommendations. 

Assuming the Auto-Doc machine is fully autonomous, should Mike’s be faulted for relying on it to correctly diagnose car problems? Is Mike’s entitled to rely on Acme’s representations about Auto-Doc’s capabilities, or would the repair shop have a duty to inquire about and/or investigate Auto-Doc’s limitations? Assuming Suzie did not know, and had no reason to suspect, her brakes were worn out, should she be faulted for relying on a fully autonomous machine instead of taking the car to a trained human mechanic?  Why or why not?

Criminal liability

It is conceivable that an AI system might engage in activity that is prohibited by an applicable jurisdiction’s criminal laws. E-mail address harvesting is an example. In the United States, for example, the CAN-SPAM Act makes it a crime to send a commercial email message to an email address that was  obtained  by automated scraping of Internet websites for email addresses. Of course, if a person intentionally uses an AI system for scraping, then liability should be clear. But what if an AI system “learns” to engage in scraping?

AI-generated criminal output may also be a problem. Some countries have made it a crime to display a Nazi symbol, such as a swastika, on a website. Will criminal liability attach if a website or blog owner uses AI to generate illustrated articles about World War II and the system generates and displays articles that are illustrated with World War II era German flags and military uniforms? In the United States, creating or possessing child pornography is illegal. Will criminal liability attach if an AI system generates it?

Some of these kinds of issues can be resolved through traditional legal analysis of the intent and scienter elements of the definitions of crimes. A jurisdiction might wish to consider, however, whether AI systems should be regulated to require system creators to implement measures that would prevent illegal uses of the technology. This raises policy and feasibility questions, such as whether and what kinds of restraints on machine learning should be required, and how to enforce them. Further, would prior restraints on the design and/or use of AI-powered expressive-content-generating systems infringe on First Amendment rights?  

Product liability

Related to the problem of allocating responsibility for harm caused by the use of an AI mechanism is the question whether anyone should be held liable for harm caused when the mechanism is not defective, that is to say, when it is operating as it should.

 Example.  Acme Co. manufactures and sells Auto-Article, a software program that is designed to create content of a type and kind the user specifies. The purpose of the product is to enable a website owner to generate and publish a large volume of content frequently, thereby improving the website’s search engine ranking. It operates   by scouring the Internet and analyzing instances of the content the user specifies to produce new content that “looks like” them. XYZ Co. uses the software to generate articles on medical topics. One of these articles explains that chest pain can be caused by esophageal spasms but that these typically do not require treatment unless they occur frequently enough to interfere with a person’s ability to eat or drink. Joe is experiencing chest pain. He does not seek medical help, however, because he read the article and therefore believes he is experiencing esophageal spasms. He later collapses and dies from a heart attack. A medical doctor is prepared to testify that his death could have been prevented if he had sought medical attention when he began experiencing the pain.

Should either Acme or XYZ Co. be held liable for Joe’s death? Acme could argue that its product was not defective. It was fit for its intended purposes, namely, a machine learning system that generates articles that look like articles of the kind a user specifies. What about XYZ Co.? Would the answer be different if XYZ had published a notice on its site that the information provided in its articles is not necessarily complete and that the articles are not a substitute for advice from a qualified medical professional? If XYZ incurs liability as a result of the publication, would it have a claim against Acme, such as for failure to warn it of the risks of using AI to generate articles on medical topics?

Consumer protection

AI system deployment raises significant health and safety concerns. There is the obvious example of an AI system making incorrect medical diagnoses or treatment recommendations. Autonomous (“self-driving”) motor vehicles are also examples. An extensive body of consumer protection regulations may be anticipated.

Forensic and evidentiary issues

In situations involving the use of semi-autonomous AI, allocating responsibility for harm resulting from the operation of the AI  system  may be difficult. The most basic question in this respect is whether an AI system was in use or not. For example, if a motor vehicle that can be operated in either manual or autonomous mode is involved in an accident, and fault or the extent of liability depends on that (See the discussion of tort liability, above), then a way of determining the mode in which the car was being driven at the time will be needed.

If, in the case of a semi-autonomous AI system, tort liability must be allocated between the creator of the system and a user of it, the question of fault may depend on who actually caused a particular tortious operation to be executed – the system creator or the user. In that event, some method of retracing the steps the AI system used may be essential. This may also be necessary in situations where some factor other than AI contributed, or might have contributed, to the injury. Regulation may be needed to ensure that the steps in an AI system’s operations are, in fact, capable of being ascertained.

Transparency problems also fall into this category. As explained in the Journal of Responsible Technology, people might be put on no-fly lists, denied jobs or benefits, or refused credit without knowing anything more than that the decision was made through some sort of automated process. Even if transparency is achieved and/or mandated, contestability will also be an issue.

Data Privacy

To the extent an AI system collects and stores personal or private information, there is a risk that someone may gain unauthorized access to it.. Depending on how the system is designed to function, there is also a risk that it might autonomously disclose legally protected personal or private information. Security breaches can cause catastrophic problems for data subjects.

Publicity rights

Many jurisdictions recognize a cause of action for violation of a person’s publicity rights (sometimes called “misappropriation of personality.”) In these jurisdictions, a person has an exclusive legal right to commercially exploit his or her own name, likeness or voice. To what extent, and under what circumstances, should liability attach if a commercialized AI system analyzes the name, likeness or voice of a person that it discovers on the Internet? Will the answer depend on how much information about a particular individual’s voice, name or likeness the system uses, on one hand, or how closely the generated output resembles that individual’s voice, name or likeness, on the other?

Contracts

The primary AI-related contract concern is about drafting agreements that adequately and effectively allocate liability for losses resulting from the use of AI technology. Insurance can be expected to play a larger role as the use of AI spreads into more areas.

Bias, Discrimination, Diversity & Inclusion

Some legislators have expressed concern that AI systems will reflect and perpetuate biases and perhaps discriminatory patterns of culture. To what extent should AI system developers be required to ensure that the data their systems use are collected from a diverse mixture of races, ethnicities, genders, gender identities, sexual orientations, abilities and disabilities, socioeconomic classes, and so on? Should developers be required to apply some sort of principle of “equity” with respect to these classifications, and if so, whose vision of equity should they be required to enforce? To what extent should government be involved in making these decisions for system developers and users?

Copyright

AI-generated works like articles, drawings, animations, music and so on, raise two kinds of copyright issues:

  1. Input issues, i.e., questions like whether AI systems that create new works based on existing copyright-protected works infringe the copyrights in those works
  2. Output issues, such as who, if anybody, owns the copyright in an AI-generated work.

I’ve written about AI copyright ownership issues and AI copyright infringement issues in previous blog posts on The Cokato Copyright Attorney.

Patents and other IP

Computer programs can be patented. AI systems can be devised to write computer programs. Can an AI-generated computer program that meets the usual criteria for patentability (novelty, utility, etc.) be patented?

Is existing intellectual property law adequate to deal with AI-generated inventions and creative works? The World Intellectual Property Organization (WIPO) apparently does not think so. It is formulating recommendations for new regulations to deal with the intellectual property aspects of AI.

Conclusion

AI systems raise a wide range of legal issues. The ones identified in this article are merely a sampling, not a complete listing of all possible issues. Not all of these legal issues have answers yet. It can be expected that more AI regulatory measures, in more jurisdictions around the globe, will be coming down the pike very soon.

Contact attorney Thomas James

Contact Minnesota attorney Thomas James for help with copyright and trademark registration and other copyright and trademark related matters.

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