Assessing AI Output in Legal Decision-Making with Nearest Neighbors

Artificial intelligence (“AI”) systems are widely used to assist or automate decision-making. However, while there are general metrics for performance of AI systems, there is as yet no well-established gauge to assess the quality of a particular AI recommendation or decision. This presents a serious problem in the emerging use of AI in legal applications because the legal system aims at 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. The method 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. The paper 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. It also presents the limitations of the concept