Explainability and the Epistemic Division of Labour in Adjudication

Author:

Chiao Vincent1,Heslop Martin2

Affiliation:

1. Tyler Haynes Professor of Interdisciplinary Studies, School of Law and Jepson School of Leadership, University of Richmond, United States

2. JD, Class of 2023, Faculty of Law, University of Toronto, Canada

Abstract

The ‘black box’ quality of contemporary algorithmic tools raises concerns related to their use in court because of the law’s emphasis on explanations, transparency, and public reasons. We argue that the problems of explainability associated with contemporary algorithmic tools are, from a legal perspective, neither sui generis nor irreconcilable with existing norms. We distinguish between the types of explanations required by fact-finders and those required from judges. We conclude that apparent tensions can be reconciled by attending to the epistemic division of labour between the legal and scientific communities, contextualizing expert evidence appropriately, and distinguishing between explanation as reconstruction and as justification.

Publisher

University of Toronto Press Inc. (UTPress)

Subject

Law,Sociology and Political Science

Reference59 articles.

1. See R v GF, 2021 SCC 20 at para 68 [GF].

2. See Violence Risk Appraisal Guide – Revised (VRAG-R) Development Committee, ‘Home,’ online: Violent Risk Appraisal Guide [perma.cc/7VJC-CSTU] (describing the VRAG-R).

3. See Equivant, ‘Practitioner’s Guide to COMPAS Core’ (4 April 2019), online: Equivant [perma.cc/4GY5-TFZV] (describing the Correctional Offender Management Profiling for Alternative Sanctions [COMPAS]).

4. The principles behind STRMix are outlined in John Buckleton et al, ‘The Probabilistic Genotyping Software STRMix: Utility and Evidence for Its Validity’ (2018) 64:2 Journal of Forensic Sciences 393 (describing the principles behind STRMix).

5. For an approachable overview of the principles behind machine learning, see Rene Y Choi et al, ‘Introduction to Machine Learning, Neural Networks, and Deep Learning’ (2020) 9:14 Translational Vision Science and Technology 1, online:

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