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.

中文翻译：

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少年司法风险评估的准确性和公平性

人们通常说在刑事司法环境中使用的风险评估算法会引入“偏见”。但是，这样的收费会使算法的性能与数据中的偏差混为一谈，而这些数据用于通过对算法的输出采取的行动中的偏差来训练算法。在本文中，算法本身就是重点。使用针对少年司法数据的算法应用，说明了不同种类的公平之间以及公平与准确性之间的权衡。在培训数据存在潜在偏差的情况下，风险评估算法可以提高公平性吗？如果可以，那么它将对准确性产生什么影响？尽管统计学家和计算机科学家可以记录这些折衷，但是他们不能提供满足所有公平性和准确性目标的技术解决方案。到底，