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

合作研究：RI：AF：小：战略性个人行为下公平机器学习的长期影响

基本信息

批准号：
2301599
负责人：
Mohammadmahdi Khaliligarekani
金额：
$ 25.35万
依托单位：
Ohio State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-11-01 至 2025-09-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2301599&HistoricalAwards=false
关键词：
Collaborative Research RI AF Small

项目摘要

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.

机器学习（ML）技术的发展已经彻底改变了社会，并在各个科学领域实现了突破。尽管有巨大的社会益处，但ML技术也引起了对人类做出决定的道德问题。显然，在许多高风险应用中，例如招聘，贷款，刑事司法和大学入学，ML技术可能会对处境不利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其视为静态框架中的两个独立问题。但是，ML技术对人口福祉的长期影响尚不清楚。由于ML算法被部署在动态环境中（即，在与ML算法相互作用时，在静态框架中开发的ML在静态框架中进行了战略性和反复的行为，而无需考虑人类的反馈影响可能会以一种无聊和潜在的危险方式行事。该项目超越了静态环境，旨在了解动态的人类ML相互作用下公平ML的长期影响。这种理解对于确保ML技术的可信度至关重要，并且可以利用设计有效的干预措施来促进长期的社会福利和公平；它可能会进一步指导决策者设计更好地为社会服务的政策。该项目在人类反复与ML系统相互作用的连续框架中研究了公平问题。在研究公平ML的长期影响时，将解决三个关键的研究问题：（1）如何严格地对个人战略行为及其对ML发展的影响进行严格模拟；（2）如何验证和分析人类行为模型；（3）可以采用哪种方法来改善长期的人类福祉？将机器学习，随机控制，游戏理论和社会科学的知识整合在一起，该项目将首先建立一个分析框架，以表征战略个人与ML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的分析，并进一步利用改善社会福利和长期公平的有效干预措施。最后，该项目将进行不同的分析和实验，以检查拟议框架和结果的鲁棒性和准确性。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评估评估的评估来支持的。