ABSTRACT

Algorithmic decision-making (ADM) promises to strengthen evidence-based decisions, particularly to better manage risks in various domains. Its use also extends to the criminal justice system where algorithmic risk assessments potentially provide very valuable evidence that can inform highly sensitive decisions. Yet, such algorithmic tools also introduce intricate problems that are tied to the fundamental question of exactly what kind and what quality of evidence they offer. This paper illustrates this problem based on a comparison of pretrial risk assessments that have been implemented statewide in the USA. The authors highlight the empirical variation in the construction, evaluation and documentation of these tools to carve out the considerable discretion involved along these dimensions. They also point to further possible ways of looking at the performance of these tools and show why evaluating the quality of the evidence delivered by algorithmic risk assessments is a far from straightforward affair.

摘要

算法决策 (ADM) 承诺加强基于证据的决策，特别是更好地管理各个领域的风险。它的用途还扩展到刑事司法系统，其中算法风险评估可能提供非常有价值的证据，可以为高度敏感的决策提供信息。然而，此类算法工具也引入了复杂的问题，这些问题与它们提供的证据的类型和质量的基本问题密切相关。本文基于对美国全州实施的审前风险评估的比较来说明这个问题。作者强调了这些工具在构建、评估和记录方面的经验变化，以在这些维度上划分出相当大的自由裁量权。