Renzo Cavani

AI in justice: Balancing Efficiency and Explicability

Introduction

Ai in justice: balancing efficiency and explicability. Explore the tension between AI efficiency and explicability in justice systems. This paper argues opaque, efficient AI can be justified in judicial procedures depending on its function.

Abstract

AI systems intended for use in judicial procedure carry a significant commitment to efficiency –understood from an instrumental perspective– for which accurate predictions are paramount. However, if an AI system has this objective, it is inevitable that explicability will inevitably decrease. This means that the program’s algorithms are not comprehensible to humans, and often, neither is its output.  From an AI ethics standpoint, AI systems applied in the field of justice should possess a significant degree of explicability, yet this compromises efficiency. Therefore, this paper aims to 1) highlight the tension between efficiency and explicability; and 2) argue that, even adopting an AI human-centred approach, opaque systems prioritizing efficiency could be justified, depending on the specific function of the system; specifically, in the greater or lesser degree of interference with the judicial decision that must be justified.

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