Abstract
Holmes’s enduring interest was in the development of the law, as indicated by the title The Path of the Law. He drew on the philosophy of science and the role of induction in forming scientific theories, and he added a new ingredient: social induction. Social induction has two parts. First, the law develops through the accumulation of cases, which arise through the actions of agents who are embedded within society and who necessarily adapt their actions to the law. This is analogous to a subfield of machine learning called reinforcement learning, the basis for DeepMind’s AlphaGo, in which the machine is trained on a dataset of its own adaptive actions. The second part of social induction is the process whereby settled legal doctrine arises out of contested judicial decisions. We argue that this second part can be formulated in terms of prediction (law is constituted, after all, by prophecies of what courts will do), and that it is therefore a suitable topic for machine learning. This suggests new ways of thinking about explainability of machine learning decisions.
Highlights
Given, and data does not accumulate through ongoing actions. This is why the field is called “machine learning ” rather than “machine doing ”! Even systems in which a learning agent’s actions affect its surroundings, for example a self-driving car whose movements will make other road-users react, the premise is that there are learnable patterns about how others behave, and learning those patterns is the goal of training, and training should happen in the factory rather than on the street
“AlphaGo,”[2] the AI created by DeepMind which in 2016 won a historic victory against top-ranking Go player Lee Seedol, is a product of reinforcement learning
Well-settled legal doctrine arises through a social process: it “embodies the work of many minds, and has been tested in form as well as substance by trained critics whose practical interest is to resist it at every step.”[5]
Summary
Holmes in The Path of the Law asked “What constitutes the law?” and answered that law is nothing more than prophecies of what the courts will do. In the standard paradigm for machine learning, there is no counterpart to the first part of Holmes’s insight of social induction—i.e., to the role of active agents embedded in society. WISCHIK given, and data does not accumulate through ongoing actions This is why the field is called “machine learning ” rather than “machine doing ”! In this chapter we will describe the links between reinforcement learning and Holmes’s insight that law develops through the actions of agents embedded in society. The second part of Holmes’s insight concerns the process whereby data turns into doctrine, the “continuum of inquiry.”[3] As case law accumulates, there emerge clusters of similar cases, and legal scholars, examining these clusters, hypothesize general principles. In the last part of this chapter we will discuss the role of legal explanation, and outline some problems with explainability in machine learning, and suggest how machine learning might learn from Holmes
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