Evaluating the Impacts of Machine Learning Algorithms on Human Decisions

这项研究项目将开发一个方法论框架和一套工具，用于实验评估机器学习算法对人类决策的影响。在当今这个数据驱动的社会，决策往往至少部分基于算法建议。每当选择看电影或选购衣服时，在线网站都会不断地向消费者提供这样的信息。该项目将开发方法来评估算法建议是否有助于人类决策者实现他们的目标，以及它们如何影响此类决策的公平性。新的方法将帮助研究人员在广泛的环境中对算法辅助的人类决策的有效性进行经验性评估。这些背景包括网上购物等个人决定，以及可能影响社会上许多人生活的医疗、金融和司法系统的决定。调查人员将应用新方法对司法判决的审前风险评估工具进行随机评估。将开发一个开源软件包，并将公开这项研究中使用的数据库。该项目将开发工具，用于实验性地评估算法建议是否有助于人类决策者实现他们的目标，以及这些建议如何影响此类决策的公平性。在方法论方面，该项目将展示如何评估机器学习算法对人类决策的准确性和公平性的影响。虽然关于算法公平性的文献越来越多，但现有的研究几乎都集中在对算法本身的准确性和公平性的评估上。然而，机器和人类都有自己的偏见，这些偏见可能会以意想不到的方式相互作用，影响最终的决定。此外，现有的公平定义没有考虑到决定可能影响个人这一事实。将要制定的方法框架将解决这些悬而未决的问题。在实质性方面，该项目将与美国的几个司法管辖区合作，分析原始的、真实世界随机对照试验(RCT)的数据。该项目将分析这些区域协调机制，以评估审前风险评估工具(PRAI)对司法裁决的影响。学术界和公共政策界越来越担心这些PRAI的潜在种族偏见。这项研究将开发和实施严格的评估方法来回答与政策相关的问题，以便对这场重要的公共政策辩论做出直接贡献。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This research project will develop a methodological framework and set of tools for experimentally evaluating the impacts of machine learning algorithms on human decisions. In today's data-driven society, decisions often are based at least in part on algorithmic recommendations. Whenever choosing movies to watch or shopping for clothes to wear, online sites are constantly feeding consumers with such information. The project will develop methodologies to evaluate whether algorithmic recommendations help human decision makers achieve their goals and how they affect the fairness of such decisions. The new methodologies will help researchers empirically evaluate the efficacy of algorithm-assisted human decision making in a wide range of settings. These settings include individual decisions such as online shopping as well as decisions in medicine, finance, and judicial systems that have the potential to affect the lives of many in society. The investigators will apply the new methods to a randomized evaluation of pretrial risk assessment instruments on judicial decisions. An open-source software package will be developed, and the databases used in this research will be made publicly available.This project will develop tools for experimentally evaluating whether algorithmic recommendations help human decision makers achieve their goals and how such recommendations affect the fairness of such decisions. On the methodological front, the project will show how to evaluate the impacts of machine learning algorithms on the accuracy and fairness of human decisions. Although there exists a growing literature on algorithmic fairness, existing research almost exclusively focuses on the evaluation of accuracy and fairness of the algorithms themselves. Machines and humans have their own biases, however, and these biases may interact in unexpected ways to influence ultimate decisions. Also, the existing definitions of fairness do not account for the fact that decisions may influence individuals. The methodological framework to be developed will address these open problems. On the substantive front, the project will analyze data on original, real-world randomized controlled trials (RCTs) in collaboration with several jurisdictions in the United States. The project will analyze these RCTs to evaluate the impacts of pretrial risk assessment instruments (PRAIs) on judicial decisions. There has been a growing concern in the academic and public-policy communities about the potential racial bias of these PRAIs. This research will develop and implement rigorous evaluation methodologies to answer policy-relevant questions so that direct contributions can be made to this important public policy debate.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.