实验性评估：在审前公共安全评估中的应用
Experimental Evaluation of Algorithm-Assisted Human Decision-Making: Application to Pretrial Public Safety Assessment
摘要：
尽管在我们的日常生活中越来越依赖全自动算法决策，但人类仍然会做出非常重要的决策。正如在商业、医疗保健和公共政策中经常看到的那样，算法产生的建议被提供给人类决策者，以指导他们的决策。虽然有大量文献对这种算法推荐的偏见和公平性进行了评估，但一个被忽视的问题是，它们是否有助于人类做出更好的决策。我们开发了一种统计方法，用于实验评估算法建议对人类决策的因果影响。我们还展示了如何检查算法建议是否提高了人类决策的公平性，并在各种设置下得出最佳决策规则。我们将提出的方法应用于有史以来第一次随机对照试验的初步数据，该试验评估了刑事司法系统中的审前公共安全评估（PSA）。《公共安全法》的一个目标是帮助法官决定哪些被捕的人应该被释放。在现有的初步数据的基础上，我们发现，提供PSA的法官对法官的决定和随后的被捕者的行为几乎没有整体的影响。然而，我们的分析产生了一些潜在的暗示性证据表明，PSA可能有助于避免对女性被捕者做出不必要的严厉决定，而无论其风险水平如何，同时鼓励法官对被认为有风险的男性被捕者做出更严格的决定。在公平性方面，《公共服务法案》似乎增加了对男性的性别偏见，而对法官裁决中现有的种族差异几乎没有影响。最后，我们发现，除非新犯罪的成本足够高，否则PSA的建议可能会不必要地严厉。
英文摘要：
Despite an increasing reliance on fully-automated algorithmic decision-making in our day-to-day lives, human beings still make highly consequential decisions. As frequently seen in business, healthcare, and public policy, recommendations produced by algorithms are provided to human decision-makers to guide their decisions. While there exists a fast-growing literature evaluating the bias and fairness of such algorithmic recommendations, an overlooked question is whether they help humans make better decisions. We develop a statistical methodology for experimentally evaluating the causal impacts of algorithmic recommendations on human decisions. We also show how to examine whether algorithmic recommendations improve the fairness of human decisions and derive the optimal decision rules under various settings. We apply the proposed methodology to preliminary data from the first-ever randomized controlled trial that evaluates the pretrial Public Safety Assessment (PSA) in the criminal justice system. A goal of the PSA is to help judges decide which arrested individuals should be released. On the basis of the preliminary data available, we find that providing the PSA to the judge has little overall impact on the judge's decisions and subsequent arrestee behavior. However, our analysis yields some potentially suggestive evidence that the PSA may help avoid unnecessarily harsh decisions for female arrestees regardless of their risk levels while it encourages the judge to make stricter decisions for male arrestees who are deemed to be risky. In terms of fairness, the PSA appears to increase the gender bias against males while having little effect on any existing racial differences in judges' decision. Finally, we find that the PSA's recommendations might be unnecessarily severe unless the cost of a new crime is sufficiently high.
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