Machine learning algorithms are becoming ubiquitous in modern life. When used to help inform human decision making, they have been criticized by some for insufficient accuracy, an absence of transparency, and unfairness. Many of these concerns can be legitimate, although they are less convincing when compared with the uneven quality of human decisions. There is now a large literature in statistics and computer science offering a range of proposed improvements. In this article, we focus on machine learning algorithms used to forecast risk, such as those employed by judges to anticipate a convicted offender's future dangerousness and by physicians to help formulate a medical prognosis or ration scarce medical care. We review a variety of conceptual, technical, and practical features common to risk algorithms and offer suggestions for how their development and use might be meaningfully advanced. Fairness concerns are emphasized.

中文翻译：

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公平风险算法

机器学习算法在现代生活中变得无处不在。当它们被用来帮助人类决策时，一些人批评它们不够准确、缺乏透明度和不公平。其中许多担忧可能是合理的，尽管与人类决策质量参差不齐相比，它们的说服力较差。现在，统计和计算机科学领域有大量文献提出了一系列改进建议。在本文中，我们重点关注用于预测风险的机器学习算法，例如法官用来预测已定罪罪犯未来危险性的算法，以及医生用来帮助制定医疗预后或分配稀缺医疗服务的算法。我们回顾了风险算法常见的各种概念、技术和实践特征，并就如何有意义地推进它们的开发和使用提供了建议。强调公平问题。