风险分数,标签偏见,除了厨房的水槽以外的一切
Michael Zanger-Tishler1, Julian Nyarko2, Sharad Goel3
1Sociology and Social Policy, Harvard University, Cambridge, MA 02138, USA.
Science advances
|March 29, 2024
概括
当预测犯罪行为时,请避免"厨房水槽"的方法. 排除与真实结果弱相关的特征,如地理位置,可以通过减轻标签偏差来提高风险评估算法的准确性.
科学领域:
- 计算机科学 计算机科学
- 犯罪学 犯罪学
- 统计 统计 统计 统计
背景情况:
- 风险评估算法通常采用"厨房水槽"方法,假设更多的数据导致更好的预测.
- 当算法预测真实结果的代理时,这种方法可能存在缺陷,引入"标签偏差".
研究的目的:
- 为了证明"厨房水槽"方法在风险评估算法中的失败,由于标签偏见.
- 提出一种排除特征的标准,以提高对真实结果的预测准确度.
主要方法:
- 分析在预测算法中出现"标签偏差"的条件.
- 基于与代理和真实结果的相关性,制定特征排除标准.
主要成果:
- 当使用代理结果时",厨房水槽"方法可以降低预测准确性.
- 建议排除与代理和真实结果相对相关的特征,取决于其他特征.
- 与真实结果微弱相关且直接导致代理结果的特征是排除的主要候选者.
结论:
- 从刑事风险评估中排除地理等特征可以改善实际犯罪行为的预测,尽管潜在地削弱了逮捕记录的预测.
- 减轻"标签偏见"对于开发更准确,更公平的风险评估工具至关重要.
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