研究指出评估美国再犯风险工具时，年龄和性别相关，种族无关。
Which Demographic Features Are Relevant for Individual Fairness Evaluation of U.S. Recidivism Risk Assessment Tools?
- 通过人类实验测试不同人口特征对个体公平性的影响
- 发现年龄和性别应纳入相似性判断，种族不应考虑
- 为司法系统公平性评估提供实证依据，适合政策制定者
尽管个体公平性在宪法上具有重要意义，但其技术标准尚未被美国州或联邦法律/规章实际应用。我们开展一项人类受试者实验，填补这一空白，评估在再犯风险评估（RRA）工具的个体公平性评价中，哪些人口统计特征是相关的。分析结果表明，个体相似性函数应考虑年龄和性别，但应忽略种族。
原文摘要 · Abstract (English)
Despite its constitutional relevance, the technical ``individual fairness'' criterion has not been operationalized in U.S. state or federal statutes/regulations. We conduct a human subjects experiment to address this gap, evaluating which demographic features are relevant for individual fairness evaluation of recidivism risk assessment (RRA) tools. Our analyses conclude that the individual similarity function should consider age and sex, but it should ignore race.
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