算法支持的累犯风险评估中人类预测的比较用户研究
A Comparative User Study of Human Predictions in Algorithm-Supported Recidivism Risk Assessment
AI总结：
本研究通过用户实验和焦点小组，发现算法支持能提升累犯风险评估的预测准确性，但显著增益仅见于专业用户，且专业人士认为自动化系统适合辅助而非完全替代人工判断。
摘要：
在本文中，我们研究了使用基于算法的风险评估工具来支持刑事累犯风险预测的效果。我们在实验中使用的工具是RiskEval（为双盲评审而更名）的机器学习版本，RiskEval是Country（为双盲评审而省略）司法部门使用的主要风险评估工具。任务是预测一个已从监狱释放的人是否会在未来两年内犯下新罪行，导致再次入狱。我们测量了（包括其他变量在内）人类预测在有和没有算法支持时的准确性。这项用户研究包括：（1）通过众包平台招募的来自不同背景的一般参与者，（2）针对性的参与者，他们是数据科学、犯罪学或社会工作的学生和从业者，以及与RiskEval一起工作的专业人士。在众多发现中，我们观察到算法支持系统性地导致所有参与者的预测更加准确，但统计上显著的增益仅出现在针对性参与者的表现中，相对于众包参与者而言。我们还与针对性研究的参与者进行了焦点小组讨论，以解释定量结果，包括专业使用RiskEval的人员。在其他评论中，专业参与者表示他们不会预见在刑事风险评估中使用完全自动化的系统，但确实认为它对于培训、标准化以及在他们特别困难的案件上微调或复核他们的预测是有价值的。
英文摘要：
In this paper, we study the effects of using an algorithm-based risk assessment instrument to support the prediction of risk of criminalrecidivism. The instrument we use in our experiments is a machine learning version ofRiskEval(name changed for double-blindreview), which is the main risk assessment instrument used by the Justice Department ofCountry(omitted for double-blind review).The task is to predict whether a person who has been released from prison will commit a new crime, leading to re-incarceration,within the next two years. We measure, among other variables, the accuracy of human predictions with and without algorithmicsupport. This user study is done with (1)generalparticipants from diverse backgrounds recruited through a crowdsourcing platform,(2)targetedparticipants who are students and practitioners of data science, criminology, or social work and professionals who workwithRiskEval. Among other findings, we observe that algorithmic support systematically leads to more accurate predictions fromall participants, but that statistically significant gains are only seen in the performance of targeted participants with respect to thatof crowdsourced participants. We also run focus groups with participants of the targeted study to interpret the quantitative results,including people who useRiskEvalin a professional capacity. Among other comments, professional participants indicate that theywould not foresee using a fully-automated system in criminal risk assessment, but do consider it valuable for training, standardization,and to fine-tune or double-check their predictions on particularly difficult cases.
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。
