CAREER: Strategic and Equity Considerations in Machine Learning

机器学习（ML）算法已被广泛应用于许多领域，不仅有助于做出预测，还有助于做出决策。这些应用程序中的许多都没有通过相同的过程生成训练和测试数据，这在很大程度上是因为部署了学习模型来制定和通知决策（而不仅仅是预测），并且数据生成过程涉及反馈循环。在这种“高风险”过程中，预测或决策在某些情况下可能产生非常高的效用结果，而在其他情况下可能产生非常低的效用结果。不同结果的效用的这些巨大差异产生了经典预测问题中通常不存在的额外问题。首先，结果效用的巨大差异将导致服从这些预测的代理试图为自己获得最高质量的结果；其次，系统设计者必须考虑他们的系统是否对所有人口群体做出类似的高效用预测。为了了解机器学习在这些环境中的运行情况，该项目正在研究学习系统在面对战略生成数据时的性能，以及在异构数据源上保证高质量预测的程度，以确保机器学习的见解适用于许多不同的人群，而不仅仅是大多数人群。为了补充该项目的技术研究，还有几个项目旨在扩大社区进入计算和机器学习领域。这包括与华盛顿大学（UW）的K12计算机科学（cs4教师）合作开发集成到统计教室的嵌入式模块，以及与华盛顿大学的STEM公平评估与研究中心一起评估这种方法有效性的计划。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Machine learning (ML) algorithms have been adopted in a huge number of domains, helping in making not only predictions but decisions. Many of these applications do not have training and test data generated via the same process, in large part because learned models are deployed to make and inform decisions (rather than just predictions), and the data-generating process involves feedback loops. In such “high-stakes" processes, predictions or decisions can produce very high-utility outcomes in some cases and very low-utility outcomes in others. These large differences in the utility of different outcomes creates additional concerns not generally present in classical prediction problems. First, large differences in the utility of outcomes will lead agents subject to these predictions to try and earn the highest-quality outcomes for themselves; second, system designers must consider whether their system makes similarly high-utility predictions for all demographic groups.To understand just how well ML operates in these environments, this project is studying the performance of learning systems in the face of strategically generated data, and the extent to which high-quality predictions can be guaranteed on heterogeneous data sources, ensuring that the insights from ML will apply to many different populations rather than just the majority population. Complementing the technical research of this project are several projects aimed to broaden the communities entering into computing and machine learning specifically. This includes the development of drop-in modules for integration into statistics classrooms, collaboratively with the University of Washington's (UW) K12 Computer Science (CS4Teachers), and a plan to evaluate the effectiveness of this approach with the UW’s Center for Evaluation & Research for STEM Equity.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.