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

机器学习（ML）技术的发展已经彻底改变了社会，并在各个科学领域取得了突破。尽管有巨大的社会效益，但机器学习技术在用于人类决策时也引起了伦理问题。很明显，在许多高风险的应用中，例如招聘、贷款、刑事司法和大学录取，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静态框架中的两个独立问题进行了研究。然而，机器学习技术对人类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其行为），因此在静态框架中开发的ML不考虑人类反馈效应可能会以意想不到的和潜在有害的方式表现。该项目超越了静态设置，旨在了解动态人机交互下公平ML的长期影响。这种理解对于确保机器学习技术的可信度至关重要，可以用于设计有效的干预措施，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设计更好地为社会服务的政策。该项目研究人类与机器学习系统重复交互的顺序框架中的公平性问题。在研究公平机器学习的长期影响时，将解决三个关键研究问题：(1)如何严格模拟个人战略行为及其对机器学习发展的影响；(2)如何验证和分析人类行为模型；(3)可以采取哪些方法来改善人类的长期福祉？该项目整合了机器学习、随机控制、博弈论和社会科学的知识，将首先建立一个分析框架，描述战略个体与机器学习之间复杂的顺序相互作用。该框架可以对人口动态的演变进行严格分析，并进一步用于开发有效的干预措施，以改善社会福利和长期公平。最后，本项目将进行不同的分析和实验，以检验所提出的框架和结果的稳健性和准确性。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。

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.