Collaborative Research: RI: AF: Small: Long-Term Impact of Fair Machine Learning under Strategic Individual Behavior

机器学习(ML)技术的发展使社会发生了革命性的变化，并使各种科学领域取得了突破。尽管ML技术带来了巨大的社会效益，但当被用于做出关于人类的决定时，也引起了伦理上的担忧。很明显，在许多高风险的应用中，如招聘、贷款、刑事司法和大学招生，ML技术可能会对弱势或边缘化的社会群体表现出偏见，或者容易受到个人战略行为的影响。最近的研究在很大程度上将这两个问题作为两个独立的问题放在一个静态的框架中进行考察。然而，ML技术对人口福祉的长期影响仍不清楚。由于ML算法部署在动态环境中(即个体在与ML算法交互时策略性地反复调整他们的行为)，在静态框架中开发的ML可能会以一种意想不到的、潜在有害的方式表现出来。这个项目超越了静态的设置，旨在了解公平的ML在动态的人与ML互动下的长期影响。这种理解对于确保ML技术的可信度至关重要，并可用于设计有效的干预措施，以促进长期的社会福利和公平；它可能进一步帮助指导决策者制定更好地服务于社会的政策。这个项目在一个顺序框架中研究公平性问题，人类反复与ML系统交互。在研究公平ML的长期影响时，将解决三个关键研究问题：(1)如何严格建模个人策略行为及其对ML发展的影响；(2)如何验证和分析人类行为模型；以及(3)可以采取哪些方法来改善长期人类福祉？这个项目综合了机器学习、随机控制、博弈论和社会科学的知识，首先建立了一个分析框架，描述了战略个体和ML之间复杂的顺序互动。这一框架可以对人口动态的演变进行严格的分析，并可进一步用于制定有效的干预措施，以改善社会福利和长期公平。最后，该项目将进行不同的分析和实验，以检验拟议框架和结果的稳健性和准确性。该奖项反映了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。

The development of Machine learning (ML) techniques have revolutionized society and enabled breakthroughs in various scientific fields. Despite the enormous social benefits, ML techniques have also caused ethical concerns when used to make decisions about humans. It has been evident that in many high-stakes applications, such as hiring, lending, criminal justice, and college admission, ML techniques may exhibit bias against disadvantaged or marginalized social groups or be vulnerable to individual strategic behavior. Recent studies have largely examined these as two separate issues in a static framework. However, the long-term impacts of ML techniques on the well-being of the population remain unclear. Since ML algorithms are deployed in a dynamic environment (i.e., individuals adapt their behaviors strategically and repeatedly as they interact with ML algorithms), ML developed in a static framework without considering human feedback effects may behave in an unanticipated and potentially harmful way. This project moves beyond static settings and aims to understand the long-term impacts of fair ML under dynamic human-ML interactions. Such an understanding is critical to ensure the trustworthiness of ML techniques and can be leveraged for designing effective interventions that promote long-term social welfare and equity; it may further help guide policymakers to design policies that better serve society. This project studies fairness problems in a sequential framework with humans repeatedly interacting with ML systems. Three key research questions will be addressed when investigating the long-term impacts of fair ML: (1) how to rigorously model individual strategic behavior and its impact on ML development; (2) how to validate and analyze the human behavioral model; and (3) what approaches can be taken to improve long-term human well-being? Integrating the knowledge from machine learning, stochastic control, game theory, and social sciences, this project will first establish an analytical framework that characterizes the complex sequential interactions between strategic individuals and ML. This framework could enable the rigorous analysis of the evolution of population dynamics and be further leveraged for developing effective interventions that improve social welfare and long-term equity. Finally, this project will conduct different analyses and experiments to examine the robustness and accuracy of the proposed framework and results.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.