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This chapter examines an interaction between technological shocks and the “rule of law.” It does so by analyzing the implications of a class of loosely related computational technologies termed “machine learning” (ML) or, rather less precisely “artificial intelligence” (AI). These tools are presently employed in the pre-adjudicative phase of enforcing of the laws, for example facilitating the selection of targets for tax and regulatory investigations (Coglianese and Lehr, 2016). They are also increasingly used during adjudication, for example, to facilitate and guide determinations of individual violence risk during pretrial bail determinations (Huq, 2019). Predictions of a general displacement of human judgment by code-driven counterparts abound (Re and Solow-Niedemann, 2019; Volokh, 2019; but see Wu, 2019). But in near equal measure, that prospect is also loudly decried. Anticipated effects on the fairness, transparency, and equity of adjudicative systems are the main grounds for such resistance (Michaels, 2019; O’Neil, 2016). Even if these criticisms are not framed explicitly in terms of the rule of law, they often overlap with, or are closely adjacent to, the normative concerns that ordinarily travel under that rubric.

Two general questions respecting the rule of law arise from these developments. The more immediately apparent one is whether these technologies, when integrated into the legal system, are themselves compatible or in conflict with the rule of law. Depending on which conception of the rule of law is deployed, the substitution of machine decision-making for human judgment can kindle objections based on transparency, predictability, bias, and procedural fairness. A first purpose of this chapter is to examine ways in which this technological shock poses such challenges. The interaction between the normative ambitions of the rule of law and ML technologies, I will suggest, is complex and ambiguous. In many cases, moreover, the more powerful normative objection to technology arises less from the bare fact of its adoption. It is rather a function of the socio-political context in which adoption occurred and the dynamic effect of technology on background disparities of power and resources. ML’s adoption likely exacerbates differences of social power and status in ways that place the rule of law under strain. Attending to this dynamic draws useful attention to a topic that has been noticed in theorizations of the rule of law (e.g., Gowder, 2016; Wilmot-Smith, 2019), but not extensively examined: the interaction between social and economic dynamics on the one hand, and the rule of law on the other.

The second question posed by new AI and ML technologies has also not been extensively discussed. Yet it is perhaps of more profound significance. Rather than focusing on the compliance of new technologies with rule-of-law values, it hinges on the implications of ML and AI technologies for how the rule of law itself is conceived or implemented. As Taekema (2020), has recently observed, many of the canonical discussions of the rule of law—including Dicey’s and Waldron’s—entangle a conceptual definition and a series of institutional entailments. Fuller (1964), Raz (1979), and Waldron (2011), for example, assume that the rule of law requires more or less specific institutional forms, including courts. They presumably also posit human judges exercising discretion and making judgments as necessary rather than optional. For these institutional entailments of the rule of law, a substitution of human for ML technologies likely has destabilizing implications. It sharpens the question whether the abstract concept of the rule of law needs to be realized by a particular institutional form. It raises a question whether instead technological change might demand amendments to the relationship between the concept(s) and the practice of the rule of law. For pre-existing normative concepts and their practical, institutional correlates may no longer hold under conditions of technological change. At the very least then, specification of institutional forms of the rule of law under such conditions raises challenges not just as a practical matter but also in terms of legal theory.

The chapter begins by presenting a brief introduction to ML’s present and likely future uses in the law’s enforcement and adjudication, noting the different considerations that apply to these contexts. (For the balance of the chapter, I focus on machine learning and use the term “ML” because it is the most pertinent new computational technology, and because the term AI is so broad that it risks confusion). I then consider challenges to rule-of-law values presented by this technological shift. In particular, the chapter explores interactions of ML as a new technology with social and economic arrangements, and the implications of this dynamic for the rule of law. Finally, I consider whether the emergence of ML should force a reconsideration of the ways in which the rule of law is conceptualized and implemented, and whether abstract and the practical reflection on the rule of law can be cleanly bifurcated.

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