AI Law and Policy, First Edition

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AI Law and Policy offers a survey of law and policy challenges introduced by artificial intelligence (AI) and other emerging technologies. As the law of AI is still nascent, this casebook explores developments in law and policy that attempt to bring AI under increased regulatory and legal oversight. It provides a panoramic snapshot of a fast-moving phenomenon in a transformative moment of history, and attempts to capture a wide-lens story of technology that is disruptive and an area of law that is undergoing the earliest stages of experimentation. The approach of this project is to offer primary source materials that tell a story: why the history of AI law and policy at the nascent stages is coterminous with how we should struggle with the project of AI law and policy over its longer trajectory.

This book surveys a range of topics that intersect with AI and the law: algorithmic bias and decision-making; self-driving vehicles and autonomous weapons, and other autonomous AI developments in the public and private spheres; federal law in the U.S., including criminal and civil liability that may stem from the deployment of AI technologies; and international law developments in AI, including an overview of the requirements of the EU’s General Data Protection Regulation (GDPR), the UK’s Data Protection Act, and future-oriented policy questions.

Rather than cover all of AI-related legal issues, the casebook aims for breadth of the evolving history of a range of AI governance concepts. The approach will ground the student in how AI must be interrogated for its governance impact through a wide range of case studies. The evolving arc of AI governance is examined through a series of primary source materials—complaints and cases, statutes, regulations, reports, press releases and policy guidance documents—that span multiple areas of law and policy, including Data Privacy, Cybersecurity, Antidiscrimination Law, Product Liability and Torts, Intellectual Property, Antitrust, National Security, and other related fields.

Benefits for instructors and students:

• Historical approach provides a collection of primary source materials that have been selected to facilitate a study of the development of AI through the lens of those who are struggling to create a body of law and policies to govern a technological movement that is still evolving.
• A wide variety of materials, such as proposed statutes and regulations, guidance documents, congressional hearings and testimony, reports, and other primary source materials, contextualizes how laws and policies are developing from a recognition of AI’s impact.
• Foundational topics are introduced first—such as algorithmic discrimination; transparency and explainability; data privacy, autonomy, and democracy; etc.—before addressing any substantive legal issues or presenting cases, to set the necessary precursor of discussion for the entire field of AI and the law.
• Modular chapters assume that academics and practitioners using this book will have different priorities in how they would like to sequence and emphasize the material presented. It may be used to teach a law school seminar, a doctrinal/survey course, or a short course/compressed course, with assessments ranging from anonymous final exams, periodic or final papers, or other methods. Faculty may present the material in a small discussion group setting, or in a more traditional doctrinal lecture format. The course material may be presented effectively in a variety of ways and in different sequences.