Algorithms for predicting recidivism are commonly used to assess a criminal defendant's likelihood of committing a crime. These predictions are used in pretrial, parole, and sentencing decisions. Proponents of these systems argue that big data and advanced machine learning make these analyses more accurate and less biased than humans. We show, however, that the widely used commercial risk assessment software COMPAS is no more accurate or fair than predictions made by people with little or no criminal justice expertise. We further show that a simple linear predictor provided with only two features is nearly equivalent to COMPAS with its 137 features.

中文翻译：

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预测累犯的准确性，公平性和限制。

预测累犯的算法通常用于评估刑事被告犯罪的可能性。这些预测用于预审，假释和量刑判决。这些系统的支持者认为，与人类相比，大数据和先进的机器学习使这些分析更准确，更不偏不倚。但是，我们证明，广泛使用的商业风险评估软件COMPAS并不比没有或完全没有刑事司法专业知识的人所做出的预测准确或公平。我们进一步表明，仅具有两个功能的简单线性预测器几乎等同于具有137个功能的COMPAS。