This chapter deals with how microeconomics can provide insights into the key challenge that artificial intelligence (AI) scientists face. This challenge is to create intelligent, autonomous agents that can make rational decisions. In this challenge, they confront two questions: what decision theory to follow and how to implement it in AI systems. This chapter provides answers to these questions and makes three contributions. The first is to discuss how economic decision theory – expected utility theory (EUT) – can help AI systems with utility functions to deal with the problem of instrumental goals, the possibility of utility function instability, and coordination challenges in multiactor and human–agent collective settings. The second contribution is to show that using EUT restricts AI systems to narrow applications, which are “small worlds” where concerns about AI alignment may lose urgency and be better labeled as safety issues. The chapter’s third contribution points to several areas where economists may learn from AI scientists as they implement EUT.

This chapter deals with how public policy can steer AI, by taking how it can impact the use of big data, one of the key inputs required for AI. Essentially, public policy can steer AI through putting conditions and limitations on data. But data itself can help improve public policy – also in the area of economic policymaking. Hence, this chapter touches on the future potential of economic policy improvements through AI. More specifically, we discuss under what conditions the availability of large data sets can support and enhance public policy effectiveness – including in the use of AI – along two main directions. We analyze how big data can help existing policy measures to improve their effectiveness and, second, we discuss how the availability of big data can suggest new, not yet implemented, policy solutions that can improve upon existing ones. The key message of this chapter is that the desirability of big data and AI to enhance policymaking depends on the goal of public authorities, and on aspects such as the cost of data collection and storage and the complexity and importance of the policy issue.

In this chapter, we describe the development of AI since World War II, noting various AI “winters” and tracing the current boom in AI back to around 2006/2007. We provide various metrics describing the nature of this AI boom. We then provide a summary and discussion of the salient research relevant to the economics of AI and outline some recent theoretical advances.

This chapter provides a motivation for this book, outlining the interests of economists in artificial intelligence, describing who this book is aimed at, and laying out the structure of the book.

In this chapter, we take the production function enriched with AI abilities from Chapter 4, and apply it to study the implications for progress in AI on growth and inequality. The crucial finding we discuss in this chapter is that understanding the nature of AI as narrow ML and its effect on key macroeconomic outcomes depends on having appropriate assumptions in growth models. In particular, we discuss the appropriateness of assuming, as most standard endogenous growth models today do, that economies are supply driven. If they are not supply driven, then demand constraints, which can arise from the diffusion of AI, may restrict growth. Through this, we show why expectations that AI will may lead to “explosive” economic growth is unlikely to materialize. We show that by considering the nature of AI as specific (and not general) AI and making appropriate assumptions that reflect the digital AI economy better, economic outcomes may be characterized by slow growth, rising inequality, and rather full employment – conditions that rather well describe economies in the West.

In this chapter, we consider the future of AI. We base our speculation on informed discussions of the implications of current socioeconomic and technological trends, and on our understanding of past digital revolutions. This allows us to provide insights on where the economy is heading, and what this may imply for economics as a science. Future avenues for research are identified.

Unjust enrichment is a plausible cause of action for individuals whose data has been collected and used without their consent, to train, develop, or improve AI systems, or which has been sold for such purposes. Disgorgement of profits may be possible in some situations where the defendant has unlawfully collected or used personal data. Gain-based remedies have a number of advantages in this context, including the fact that it may be relatively easy to ascertain the gain, but demonstrating the loss will be considerably harder. However, contractual pre-emption may limit the utility of claims for unjust enrichment.

Financial supervisors have begun to use AI to prevent financial distress, detect fraud and, more generally, for investor protection purposes. Similarly, private parties increasingly rely on AI to decide small claims and arbitration cases. In view of this evolution, this chapter deals with the current use of AI in the financial sector, regulation of and by AI, and, most importantly, AI-driven financial supervision.

This Handbook brings together a global team of private law experts and computer scientists to examine the interface between private law and AI, which includes issues such as whether existing private law can address the challenges of AI and whether and how private law needs to be reformed to reduce the risks of AI while retaining its benefits.