Accuracy and Fairness for Juvenile Justice Risk Assessments
Accuracy and Fairness for Juvenile Justice Risk Assessments
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DOI：
10.1111/jels.12206
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发表时间：
2019-03-01
影响因子：
1.7
通讯作者：
Berk, Richard
中科院分区：
文献类型：
--
作者：
Berk, Richard
在刑事司法环境中使用的风险评估算法通常被认为会引入"偏见"。“但是，这种指控可能会将算法的性能与用于训练算法的数据中的偏见与算法输出所采取的行动中的偏见混为一谈。在本文中，算法本身是重点。不同类型的公平性之间的权衡和公平性和准确性之间的说明使用算法应用到少年司法数据。考虑到训练数据中的潜在偏差，风险评估算法能否提高公平性，如果可以，对准确性有什么影响？虽然统计学家和计算机科学家可以记录权衡，但他们无法提供满足所有公平性和准确性目标的技术解决方案。最后，福尔斯要像往常一样，利用法律的和立法程序来实现必要的平衡。
Risk assessment algorithms used in criminal justice settings are often said to introduce "bias." But such charges can conflate an algorithm's performance with bias in the data used to train the algorithm with bias in the actions undertaken with an algorithm's output. In this article, algorithms themselves are the focus. Tradeoffs between different kinds of fairness and between fairness and accuracy are illustrated using an algorithmic application to juvenile justice data. Given potential bias in training data, can risk assessment algorithms improve fairness and, if so, with what consequences for accuracy? Although statisticians and computer scientists can document the tradeoffs, they cannot provide technical solutions that satisfy all fairness and accuracy objectives. In the end, it falls to stakeholders to do the required balancing using legal and legislative procedures, just as it always has.