Explainable AI and Law: An Evidential Survey

Karen McGregor Richmond, Satya Mahesh Muddamsetty, Thomas Gammeltoft-Hansen, Henrik Palmer Olsen, Thomas B. Moeslund

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

Decisions made by legal adjudicators and administrative decision-makers often found upon a reservoir of stored experiences, from which is drawn a tacit body of expert knowledge. Such expertise may be implicit and opaque, even to the decision-makers themselves, and generates obstacles when implementing AI for automated decision-making tasks within the legal field, since, to the extent that AI-powered decision-making tools must found upon a stock of domain expertise, opacities may proliferate. This raises particular issues within the legal domain, which requires a high level of accountability, thus transparency. This requires enhanced explainability, which entails that a heterogeneous body of stakeholders understand the mechanism underlying the algorithm to the extent that an explanation can be furnished. However, the “black-box” nature of some AI variants, such as deep learning, remains unresolved, and many machine decisions therefore remain poorly understood. This survey paper, based upon a unique interdisciplinary collaboration between legal and AI experts, provides a review of the explainability spectrum, as informed by a systematic survey of relevant research papers, and categorises the results. The article establishes a novel taxonomy, linking the differing forms of legal inference at play within particular legal sub-domains to specific forms of algorithmic decision-making. The diverse categories demonstrate different dimensions in explainable AI (XAI) research. Thus, the survey departs from the preceding monolithic approach to legal reasoning and decision-making by incorporating heterogeneity in legal logics: a feature which requires elaboration, and should be accounted for when designing AI-driven decision-making systems for the legal field. It is thereby hoped that administrative decision-makers, court adjudicators, researchers, and practitioners can gain unique insights into explainability, and utilise the survey as the basis for further research within the field.
Original languageEnglish
JournalDigital Society
Volume3
Issue number1
Number of pages33
DOIs
Publication statusPublished - 2024

Bibliographical note

This research is funded by the Danish National Research Foundation Grant no. DNRF169 and conducted under the auspices of the Danish National Research Foundation’s Centre of Excellence for Global Mobility Law.

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