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Is Law Computable?, 2020

This unique volume collects original papers by a group of leading international scholars to address some of the fascinating questions raised by the encroachment of Artificial Intelligence (AI) into more aspects of legal process, administration, and culture. Weighing near-term benefits against the longer-term, and potentially path-dependent, implications of replacing human legal authority with computational systems, this volume pushes back against the more uncritical accounts of AI in law and the eagerness of scholars, governments, and LegalTech developers, to overlook the more fundamental - and perhaps 'bigger picture' - ramifications of computable law.

With contributions by Simon Deakin, Christopher Markou, Mireille Hildebrandt, Roger Brownsword, Sylvie Delacroix, Lyria Bennet Moses, Ryan Abbott, Jennifer Cobbe, Lily Hands, John Morison, Alex Sarch, Giovanni Sartor, and Dilan Thampapillai.

This paper is the introductory chapter of Is Law Computable? Critical Perspectives on Law + Artificial Intelligence (Hart 2020).

Cross-Disciplinary Journal of Computaitonal Law, 2020

We approach the issue of interpretability in AI and law through the lens of evolutionary theory. ... more We approach the issue of interpretability in AI and law through the lens of evolutionary theory. From this perspective, blind or mindless 'direct fitting' is an iterative process through which a system and its environment are mutually constituted and aligned. The core case is natural selection. Legal reasoning can be understood as a step in the 'direct fitting' of law, through a cycle of variation, selection and retention, to its social context. Machine learning, in so far as it relies on error correction through 'backpropagation', can be used to model the same process. It may therefore have value for understanding the long-run dynamics of legal and social change. This is distinct, however, from any use it may have in predicting case outcomes. Legal interpretation in the context of the individual or instant case requires use of the higher-order cognitive capacities which human beings have evolved to understand their world and represent it through natural language. This type of forward propagation is unlikely, by its nature, to be well captured by machine learning approaches.

The use of machine learning (ML) to replicate aspects of legal decision making is already well ad... more The use of machine learning (ML) to replicate aspects of legal decision making is already well advanced, with various 'Legal Tech' applications being used to model litigation risk, and data analytics informing decisions on issues with relevance to law which include probation, predictive policing and credit evaluation. The next step, already being trialled in a number of jurisdictions, will be the use of ML to replicate core functions of legal systems, including adjudication.

We consider the likely consequences of this step using a systemic-evolutionary model of law. From this point of view, many aspects of legal reasoning have algorithmic features which could lend themselves to automation. However, an evolutionary perspective also points to features of legal reasoning which are not consistent with ML: these include the reflexivity of legal knowledge and the incompleteness of legal rules at the point where they encounter the 'chaotic' and unstructured data generated by other social subsystems. We illustrate this point with an example taken from labour law concerning the classification of work relationships.

We argue that the goal of a 'legal singularity' which has been advanced by advocates of the use of ML in law is based on a conception of a functionally complete legal system which, while a mirage, has the potential to divert resources to ultimately fruitless uses, while compromising the autonomy of the legal system and undermining its core modes of operation. Finding the institutional means to maintain law's system-boundary with technology is an urgent task but one whose success cannot be guaranteed, as there is no principle of societal organisation which guarantees the perpetuation of the rule of law, and the democratic-liberal order it maintains, in the face of current technological changes.

Papers by Christopher P Markou

What does computable law mean for the autonomy, authority, and legitimacy of the legal system? Ar... more What does computable law mean for the autonomy, authority, and legitimacy of the legal system? Are we witnessing a shift from Rule of Law to a new Rule of Technology? Should we even build these things in the first place? This unique volume collects original papers by a group of leading international scholars to address some of the fascinating questions raised by the encroachment of Artificial Intelligence (AI) into more aspects of legal process, administration, and culture. Weighing near-term benefits against the longer-term, and potentially path-dependent, implications of replacing human legal authority with computational systems, this volume pushes back against the more uncritical accounts of AI in law and the eagerness of scholars, governments, and LegalTech developers, to overlook the more fundamental - and perhaps 'bigger picture' - ramifications of computable law. With contributions by Simon Deakin, Christopher Markou, Mireille Hildebrandt, Roger Brownsword, Sylvie Delacroix, Lyria Bennet Moses, Ryan Abbott, Jennifer Cobbe, Lily Hands, John Morison, Alex Sarch, Giovanni Sartor, and Dilan Thampapillai. This paper is the introductory chapter of Is Law Computable? Critical Perspectives on Law + Artificial Intelligence (Hart 2020).

SSRN Electronic Journal, 2020

We approach the issue of interpretability in AI and law through the lens of evolutionary theory. ... more We approach the issue of interpretability in AI and law through the lens of evolutionary theory. From this perspective, blind or mindless ‘direct fitting’ is an iterative process through which a system and its environment are mutually constituted and aligned. The core case is natural selection. Legal reasoning can be understood as a step in the ‘direct fitting’ of law, through a cycle of variation, selection and retention, to its social context. Machine learning, in so far as it relies on error correction through ‘backpropagation’, can be used to model the same process. It may therefore have value for understanding the long-run dynamics of legal and social change. This is distinct, however, from any use it may have in predicting case outcomes. Legal interpretation in the context of the individual or instant case requires use of the higher-order cognitive capacities which human beings have evolved to understand their world and represent it through natural language. This type of forward propagation is unlikely, by its nature, to be well captured by machine learning approaches.

SSRN Electronic Journal, 2019

Is Law Computable?, 2020

This paper is the introductory chapter of Is Law Computable? Critical Perspectives on Law + Artificial Intelligence (Hart 2020).

Cross-Disciplinary Journal of Computaitonal Law, 2020

SSRN Electronic Journal, 2020

We approach the issue of interpretability in AI and law through the lens of evolutionary theory. ... more We approach the issue of interpretability in AI and law through the lens of evolutionary theory. From this perspective, blind or mindless ‘direct fitting’ is an iterative process through which a system and its environment are mutually constituted and aligned. The core case is natural selection. Legal reasoning can be understood as a step in the ‘direct fitting’ of law, through a cycle of variation, selection and retention, to its social context. Machine learning, in so far as it relies on error correction through ‘backpropagation’, can be used to model the same process. It may therefore have value for understanding the long-run dynamics of legal and social change. This is distinct, however, from any use it may have in predicting case outcomes. Legal interpretation in the context of the individual or instant case requires use of the higher-order cognitive capacities which human beings have evolved to understand their world and represent it through natural language. This type of forward propagation is unlikely, by its nature, to be well captured by machine learning approaches.

SSRN Electronic Journal, 2019