Evolutionary Interpretation: Law and Machine Learning

Journal of Cross-Disciplinary Research in Computational Law (Forthcoming)

University of Cambridge Faculty of Law Research Paper Forthcoming

19 Pages Posted: 3 Dec 2020

See all articles by Simon Deakin

Simon Deakin

University of Cambridge - Centre for Business Research (CBR); European Corporate Governance Institute (ECGI); University of Cambridge - Faculty of Law

Christopher Markou

University of Cambridge - Faculty of Law; University of Cambridge - Centre for Business Research (CBR)

Date Written: November 17, 2020

Abstract

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.

Keywords: Law, Legal Evolution, Artificial Intelligence, Machine Learning, Legal Reasoning, Legal Interpretation, Data Science, Natural Language Processing

JEL Classification: C00, C01, C7, C80, K4, K10, K30, K34, K41, L5, L51, L52, P11

Suggested Citation

Deakin, Simon F. and Markou, Christopher, Evolutionary Interpretation: Law and Machine Learning (November 17, 2020). Journal of Cross-Disciplinary Research in Computational Law (Forthcoming), University of Cambridge Faculty of Law Research Paper Forthcoming, Available at SSRN: https://ssrn.com/abstract=3732115 or http://dx.doi.org/10.2139/ssrn.3732115

Simon F. Deakin

University of Cambridge - Centre for Business Research (CBR) ( email )

Top Floor, Judge Business School Building
Trumpington Street
Cambridge, CB2 1AG
United Kingdom
+ 44 1223 335243 (Phone)

European Corporate Governance Institute (ECGI)

c/o the Royal Academies of Belgium
Rue Ducale 1 Hertogsstraat
1000 Brussels
Belgium

HOME PAGE: http://www.ecgi.org

University of Cambridge - Faculty of Law ( email )

10 West Road
Cambridge, CB3 9DZ
United Kingdom

Christopher Markou (Contact Author)

University of Cambridge - Faculty of Law ( email )

10 West Road
Cambridge, CB3 9DZ
United Kingdom

HOME PAGE: http://https://www.law.cam.ac.uk/people/academic/cp-markou/6574

University of Cambridge - Centre for Business Research (CBR) ( email )

Top Floor, Judge Business School Building
Trumpington Street
Cambridge, CB2 1AG
United Kingdom

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
208
Abstract Views
731
rank
186,359
PlumX Metrics