Assessing AI output in legal decision-making with nearest neighbors

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Serval ID
serval:BIB_9CCCD082C764
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Assessing AI output in legal decision-making with nearest neighbors
Journal
Penn State Law Review
Author(s)
Lau Timothy, Biedermann Alex
ISSN
0012-2459
Publication state
Published
Issued date
01/07/2020
Volume
124
Number
3
Pages
609–655
Language
english
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.
Keywords
Artificial Intelligence, AI output, Legal decision-making
Open Access
Yes
Funding(s)
Swiss National Science Foundation / BSSGI0_155809
Create date
05/07/2020 21:03
Last modification date
06/07/2020 6:09
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