Three Laws Blog: Law and Artificial Intelligence

Shaheen on Section 230's Immunity for Generative Artifiicial Intelligence

Louis Shaheen, Ohio State University, has published Section 230's Immunity for Generative Artificial Intelligence. Here is the abstract.

Section 230 of the Communications Decency Act of 1996 immunizes online platforms from liability resulting from the content that third parties post on their platforms. Congress passed Section 230 in response to two civil cases from New York that split on whether online platforms were immune from liability that third parties posted on their sites. Congress was concerned that, without immunity from third-party content posted on their websites, online platforms would not survive. Moreover, Congress believed that immunity was necessary for the internet's development. Congress was right. Commentators have hailed Section 230 as the backbone of the internet. But as time has passed, Section 230 has been repeatedly criticized for the broad immunity it has given platforms––immunity for conduct outside the bounds of what its drafters anticipated. With the emergence of generative artificial intelligence, Section 230 is back in the news. Congress, President Joe Biden, law firms, and law professors have begun discussing in executive orders, hearings, and law reviews whether generative artificial intelligence has Section 230 immunity. They are all concerned about the impact that artificial intelligence will have on our society. Circuit courts have not had the chance to explicitly make clear whether Section 230 immunizes generative artificial intelligence.

Download the article from SSRN at the link.

Grieman on Law, Death, and Robots: The Regulation of Artificial Intelligence in High Risk Civil Applications @hartpublishing

Forthcoming from Hart Publishing:

Keri Grieman, Law, Death, and Robots: The Regulation of Artificial Intelligence in High Risk Civil Applications (October 2024). Here from the publisher's website is a description of the book's contents.

 

Can the law keep up with AI? This book examines liability and regulation for artificial intelligence causing serious physical harm, both now and in the future. While AI moves quickly, regulation follows more slowly – an increasing problem for an evolutionary, fast-paced emerging technology. AI has the potential to save lives, but in doing so will have the potential to take them as well. How do we future-proof law and regulation to incentivise life-saving innovation as safely as possible? This book details how to regulate AI in high-risk civil applications (for example, automated vehicles and medicine), addressing both liability and regulatory structure. It highlights crucial liability themes for technology governance; provides tools to bridge the gap between regulators and technologists; examines jurisdictional approaches to AI regulation in the EU, UK, USA, and Singapore; and ultimately suggests a jurisdiction-agnostic blueprint for regulation.

Maliha, Gerke, Parikh, and Cohen on Spurring Growth in AI By Finding a New Approach to Legal Liability @George_Maliha @gerke_sara @CohenProf @HarvardBiz @DickinsonLaw @Harvard_Law

George Maliha, Sara Gerke, Dickinson Law, Ravi B. Parikh, and I. Glenn Cohen, Harvard Law School are publishing To Spur Growth in AI, We Need a New Approach to Legal Liability in the Harvard Business Review (2021). Here is the abstract.

The existing liability system in the United States and other countries can’t handle the risks relation to AI. That’s a problem because it will slow AI innovation and adoption. The answer is to revamp the system, which involves revising standards of care, changing who compensates parties when inevitable accidents occur via insurance and indemnity; changing default liability options; creating new adjudicators; and revamping regulations to prevent mistakes and exempt certain kinds of liability.

The full text is not available for download from SSRN.

Banteka on Artificially Intelligent Persons @nbanteka

Nadia Banteka, Villanova University School of Law, is publishing Artificially Intelligent Persons in the Houston Law Review (volume 58) (2020). Here is the abstract.

Artificial Intelligence (AI) entities seriously challenge traditional legal frameworks for attribution and liability because they operate at an increasing distance from their developers and owners, resulting in accountability gaps. Consider a scenario in which a self-driving car causes injury or even death to a human. Who do we hold accountable? We have no clear answer as to who can be sued or prosecuted because we lack a comprehensive legal understanding of AI entities. Many scholars propose as a solution to the accountability problem attaching liability to the direct source of the harm, the AI entity itself, by first granting it legal personhood. But the law has yet to answer the question of whether AI entities qualify for legal personhood and, if so, on what legal basis. This article is the first to empirically assess the scope of legal personhood as it relates to AI entities, and to answer this question. I make two claims about the problem of legal personhood for AI. First, I argue that the courts’ overall approach to legal personhood has been more disparate than many have assumed, and it does not support legal personhood for AI entities. To substantiate this position, I analyze the legal basis for judicial decisions conferr ing legal personhood on artificial entities across U.S. courts from 1809 to the present, and I offer a statistical analysis of the frequency with which different conditions for legal personhood appear in these decisions. I find a clear dissonance between legal doctrine and existing theory on legal personhood for AI entities. Second, I argue that empirically understanding the legal landscape for legal personhood prevents courts from conferring legal personhood on AI entities, and should give legislators pause before doing so. If courts and legislators consider the conditions for legal personhood that this article identifies in answering questions of liability for AI entities, they will discover the incompatibility between legal personhood and these entities. Without recognition of this incompatibility, theory, policy, and litigation surrounding AI entities could move in a direction that undermines legal certainty and upsets legal expectation.

Download the article from SSRN at the link.B

Wu on Autonomous Vehicles, Trolley Problems, and the Law @stephenswu

Stephen S. Wu, Silicon Valley Law Group, has published Autonomous Vehicles, Trolley Problems, and the Law, in Ethics and Information Technology, 2019, at 1 (online).

Diamantis on The Extended Corporate Mind: When Corporations Use AI To Break the Law @IowaLawSchool

Mihailis Diamantis, University of Iowa College of Law, has published The Extended Corporate Mind: When Corporations Use AI to Break the Law. Here is the abstract.

Algorithms may soon replace employees as the leading cause of corporate harm. For centuries, the law has defined corporate misconduct — anything from civil discrimination to criminal insider trading — in terms of employee misconduct. Today, however, breakthroughs in artificial intelligence and big data allow automated systems to make many corporate decisions, e.g., who gets a loan or what stocks to buy. These technologies introduce valuable efficiencies, but they do not remove (or even always reduce) the incidence of corporate harm. Unless the law adapts, corporations will become increasingly immune to civil and criminal liability as they transfer responsibility from employees to algorithms. This Essay is the first to tackle the full extent of the growing doctrinal gap left by algorithmic corporate misconduct. To hold corporations accountable, the law must sometimes treat them as if they “know” information stored on their servers and “intend” decisions reached by their automated systems. Cognitive science and the philosophy of mind offer a path forward. The “extended mind thesis” complicates traditional views about the physical boundaries of the mind. The thesis states that the mind encompasses any system that sufficiently assists thought, e.g. by facilitating recall or enhancing decision-making. For natural people, the thesis implies that minds can extend beyond the brain to include external cognitive aids, like rolodexes and calculators. This Essay adapts the thesis to corporate law. It motivates and proposes a doctrinal framework for extending the corporate mind to the algorithms that are increasingly integral to corporate thought. The law needs such an innovation if it is to hold future corporations to account for their most serious harms.

Download the article from SSRN at the link.

Newly Published: Mark Chinen, Law and Autonomous Machines (Elgar) @MarkChinen

Newly published: Mark Chinen, Seattle University School of Law, Law and Autonomous Machines: The Co-evolution of Legal Responsibility and Technology (Elgar Publishing, 2019) (Elgar Law, Technology, and Society). Here from the publisher's website is a description of the book's contents.

This book sets out a possible trajectory for the co-development of legal responsibility on the one hand and artificial intelligence and the machines and systems driven by it on the other. As autonomous technologies become more sophisticated it will be harder to attribute harms caused by them to the humans who design or work with them. This will put pressure on legal responsibility and autonomous technologies to co-evolve. Mark Chinen illustrates how these factors strengthen incentives to develop even more advanced systems, which in turn strengthens nascent calls to grant legal and moral status to autonomous machines. This book is a valuable resource for scholars and practitioners of legal doctrine, ethics, and autonomous technologies.

Rachum-Twaig on Liability For AI-Based Robots @OmriRT

Omri Rachum-Twaig, Tel Aviv University, Buchmann Faculty of Law; Federman Cyber Security Research Center, Hebrew University of Jerusalem, is publishing Whose Robot Is It Anyway?: Liability for Artificial-Intelligence-Based Robots in the University of Illinois Law Review (2020). Here is the abstract.

The idea of robots that possess autonomous capabilities and intelligence and are ungoverned by human directions or supervision dates as far back as several decades ago. These (then) futuristic ideas were steadily incorporated into very real and current technology. Combined with Artificial Intelligence (AI) technology, products and machines disrupt the idea of agency and the involvement of human beings in manufacturing and in provision of services. How should liability be constructed when there is no apparent agency or personhood or when actions are almost inherently unforeseeable? More specifically, in the context of AI-based robots, do models of products liability or other tort liability fit the new framework? This Article seeks to explain why current law and doctrine, such as products liability and negligence, cannot provide an adequate framework for these technological advancements, mainly due to the lack of personhood and agency and to the inability to predict and explain robot behavior. Zooming out from specific doctrines, the Article also suggests that none of the three main liability regimes — strict liability, negligence, and no-fault mandatory insurance — adequately resolves the challenges of AI-based robots. Ultimately, this Article aims at suggesting supplementary rules that, together with existing liability models, could provide better legal structures that fit AI-based robots. Such supplementary rules will function as quasi-safe harbors or predetermined levels of care. Meeting them would shift the burden back to current tort doctrines. Failing to meet such rules would lead to liability. Such safe harbors may include a monitoring duty, built-in emergency breaks, and ongoing support and patching duties. The argument is that these supplementary rules could be used as a basis for presumed negligence that complements the existing liability models. If adopted, they could establish clear rules or best practices that determine the scope of potential liability of designers, operators, and end-users of AI-based robots.

Download the article from SSRN at the link.

Morse on When Robots Make Legal Mistakes

Susan C. Morse, University of Texas, Austin, School of Law, is publishing When Robots Make Legal Mistakes in the Oklahoma Law Review. Here is the abstract.

The questions presented by robots’ legal mistakes are familiar to legal scholarship because they are examples of the legal process inquiry described by Hart and Sacks. This inquiry asks when the law will accept decisions as final, even if they are mistaken. Legal decision-making robots include market robots and government robots. In either category, they can make mistakes of undercompliance or overcompliance. Some robot mistakes are more reviewable than others. The least reviewable kind of mistake is a market robot’s overcompliance mistake. A government robot’s undercompliance mistake is also relatively unlikely to be challenged. On the other hand, a market robot’s undercompliance mistake is open to a government enforcement challenge, and a government robot’s overcompliance mistake is open to a challenge from an aggrieved regulated party. If robot systems are not explainable, or if courts do not recognize their way of explaining as legitimate under the law, then robots will be less able to defend their actions. For instance, robots will be less able to show that their actions are reasonable, nonnegligent, or consistent with due process requirements. If robots cannot defend their legal decisions due to a lack of explainability, they may respond by making decisions that will avoid the prospect of challenge. This incentive could produce counterintuitive results. For instance, it could encourage market robots to overcomply and government robots to undercomply with the law.

Download the article from SSRN at the link.

Lemley and Casey on Remedies For Robots

Mark A. Lemley and Bryan Casey, both of Stanford Law School, have published Remedies for Robots as Stanford Law and Economics Olin Working Paper No. 523. Here is the abstract.

What happens when artificially intelligent robots misbehave? The question is not just hypothetical. As robotics and artificial intelligence (AI) systems increasingly integrate into our society, they will do bad things. They have already killed people. These new technologies present a number of interesting substantive law questions, from predictability, to transparency, to liability for high stakes decision making in complex computational systems. Our focus here is different. We seek to explore what remedies the law can and should provide once a robot has caused harm. Where substantive law defines who wins legal disputes, remedies law asks, “What do I get when I win?” Remedies are sometimes designed to make plaintiffs whole by restoring them to the condition they would have been in “but for” the wrong. But they can also contain elements of moral judgment, punishment, and deterrence. For instance, the law will often act to deprive a defendant of its gains even if the result is a windfall to the plaintiff, because we think it is unfair to let defendants keep those gains. In other instances, the law may order defendants to do (or stop doing) something unlawful or harmful. Each of these goals of remedies law, however, runs into difficulties when the bad actor in question is neither a person nor a corporation but a robot. We might order a robot—or, more realistically, the designer or owner of the robot—to pay for the damages it causes. (Though, as we will see, even that presents some surprisingly thorny problems.) But it turns out to be much harder for a judge to “order” a robot, rather than a human, to engage in or refrain from certain conduct . Robots can’t directly obey court orders not written in computer code. And bridging the translation gap between natural language and code is often harder than we might expect. This is particularly true of modern AI techniques that empower machines to learn and modify their decision making over time. If we don’t know how the robot “thinks,” we won’t know how to tell it to behave in a way likely to cause it to do what we actually want it to do. Moreover, if the ultimate goal of a legal remedy is to encourage good behavior or discourage bad behavior, punishing owners or designers for the behavior of their robots may not always make sense—if only for the simple reason that their owners didn’t act wrongfully in any meaningful way. The same problem affects injunctive relief. Courts are used to ordering people and companies to do (or stop doing) certain things, with a penalty of contempt of court for noncompliance. But ordering a robot to abstain from certain behavior won’t be trivial in many cases. And ordering it to take affirmative acts may prove even more problematic. In this paper, we begin to think about how we might design a system of remedies for robots. It may, for example, make sense to focus less of our doctrinal attention on moral guilt and more of it on no-fault liability systems (or at least ones that define fault differently) to compensate plaintiffs. But addressing payments for injury solves only part of the problem. Often we want to compel defendants to do (or not do) something in order to prevent injury. Injunctions, punitive damages, and even remedies like disgorgement are all aimed, directly or indirectly, at modifying or deterring behavior. But deterring robot misbehavior too is going to look very different than deterring humans. Our existing doctrines often take advantage of “irrational” human behavior like cognitive biases and risk aversion. Courts, for instance, can rely on the fact that most of us don’t want to go to jail, so we tend to avoid conduct that might lead to that result. But robots will be deterred only to the extent that their algorithms are modified to include sanctions as part of the risk-reward calculus. These limitations may even require us to institute a “robot death penalty” as a sort of specific deterrence against certain bad behaviors. Today, speculation of this sort may sound far-fetched. But the field already includes examples of misbehaving robots being taken offline permanently—a trend which only appears likely to increase in the years ahead. Finally, remedies law also has an expressive component that will be complicated by robots. We sometimes grant punitive damages—or disgorge ill-gotten gains—to show our displeasure with you. If our goal is just to feel better about ourselves, perhaps we might also punish robots simply for the sake of punishing them. But if our goal is to send a slightly more nuanced signal than that through the threat of punishment, robots will require us to rethink many of our current doctrines. It also offers important insights into the law of remedies we already apply to people and corporations.

Download the article from SSRN at the link.