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.

Legal technologies using AI-augmented algorithms to translate the purpose of a law into a specific legal directive can be used to produce self-driving contracts, that is, a contract which instead of relying on a human referee to fill gaps, update, or reform the provisions of the contract, uses data-driven predictive algorithms to do so instead. Self-driving contracts are not simply science fiction; not only are self-driving contracts possible, they are in fact already with us.

The law should be ‘computable’, in order to make retrieval and analysis easier. Computable law takes aspects of law, which are implicit in legal texts, and aims to model them as data, rules, or forms which are amenable to computer processes. Laws should be labelled with computable structural data to permit advanced computational processing and legal analysis.