军事风险评估可以改变高风险政府环境中的人类决策过程
Algorithmic Risk Assessments Can Alter Human Decision-Making Processes in High-Stakes Government Contexts
摘要：
各国政府在做出重要决策时越来越多地转向算法风险评估，比如是否在审判前释放刑事被告。政策制定者断言，为公务员提供算法建议将改善人类风险预测，从而导致更好的（例如，更公平的决定。然而，由于许多政策决策需要在降低风险与竞争目标之间取得平衡，因此提高预测的准确性并不一定会提高决策的质量。如果风险评估让人们更加关注以牺牲其他价值为代价来降低风险，那么这些算法将减少公共政策的实施，即使它们导致更准确的预测。通过一项有2,140名外行参与者模拟两个高风险政府环境的实验，我们提供了第一个直接证据，证明风险评估可以系统地改变人们如何将风险因素纳入决策。这些变化抵消了提高预测准确性的潜在好处。在我们实验的审前设置中，风险评估使参与者对感知风险的增加更加敏感;这种转变使审前拘留中的种族差异增加了1.9%。在我们实验中的政府贷款设置中，风险评估使参与者更加厌恶风险;这一转变使政府援助减少了8.3%。这些结果表明，试图通过将预测算法纳入多方面的政策决策来改善公共政策的潜在限制和危害。如果这些观察到的行为在实践中发生，向公务员提交风险评估将在公共政策中产生意想不到的和不公正的转变，而不受民主审议或监督。
英文摘要：
Governments are increasingly turning to algorithmic risk assessments when making important decisions, such as whether to release criminal defendants before trial. Policymakers assert that providing public servants with algorithmic advice will improve human risk predictions and thereby lead to better (e.g., fairer) decisions. Yet because many policy decisions require balancing risk-reduction with competing goals, improving the accuracy of predictions may not necessarily improve the quality of decisions. If risk assessments make people more attentive to reducing risk at the expense of other values, these algorithms would diminish the implementation of public policy even as they lead to more accurate predictions. Through an experiment with 2,140 lay participants simulating two high-stakes government contexts, we provide the first direct evidence that risk assessments can systematically alter how people factor risk into their decisions. These shifts counteracted the potential benefits of improved prediction accuracy. In the pretrial setting of our experiment, the risk assessment made participants more sensitive to increases in perceived risk; this shift increased the racial disparity in pretrial detention by 1.9%. In the government loans setting of our experiment, the risk assessment made participants more risk-averse; this shift reduced government aid by 8.3%. These results demonstrate the potential limits and harms of attempts to improve public policy by incorporating predictive algorithms into multifaceted policy decisions. If these observed behaviors occur in practice, presenting risk assessments to public servants would generate unexpected and unjust shifts in public policy without being subject to democratic deliberation or oversight.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
