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Abstract

Artificial intelligence (“AI”) systems are widely used to assist or automate decision-making. Although there are general metrics for the performance of AI systems, there is, as yet, no well-established gauge to assess the quality of particular AI recommendations or decisions. This presents a serious problem in the emerging use of AI in legal applications because the legal system aims for good performance not only in the aggregate but also in individual cases. This Article presents the concept of using nearest neighbors to assess individual AI output. This nearest neighbor analysis has the benefit of being easy to understand and apply for judges, lawyers, and juries. In addition, it is fundamentally compatible with existing AI methodologies. This Article explains how the concept could be applied for probing AI output in a number of use cases, including civil discovery, risk prediction, and forensic comparison, while also presenting its limitations.

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