FAI: FairGame: An Audit-Driven Game Theoretic Framework for Development and Certification of Fair AI

人工智能技术对真实应用程序的影响越来越大，这些应用程序受到了前所未有的审查。主要的担忧之一是，这些技术在多大程度上复制或加剧了不平等，一些引人注目的例子，例如累犯率预测方面的偏差，说明了人工智能的潜在局限性，并侵蚀了人们对人工智能的信任。虽然已经出现了旨在保证人工智能系统某种形式的公平性的方法，但大多数方法仅限于相对简单的预测问题，而没有考虑预测的具体用例。然而，预测模型的许多实际应用涉及随着时间的推移而发生的决策，这些决策是通过解决复杂的优化问题而获得的。此外，即使是确定动态决策的公平结果的一般办法也很少，更不用说为确保这种情况下的公平提供指导了。为了解决这些限制，该项目正在开发一个名为FairGame的框架，用于开发和认证公平的自主决策算法。该项目还将在华盛顿大学开发新的课程和课程模块，在计算和数据科学的新跨学科计划中发挥主导作用，寻求向政策制定者和监管机构提供有关确保公平的计算方法的信息，并努力通过例如密苏里州路易斯·斯托克斯少数人参与联盟扩大对计算的参与。该项目开发了一个审计驱动的博弈论框架，用于开发和认证公平的自主决策算法。公平游戏的特点是一个计算决策策略的决策模块，以及一个伪对抗性审计师，它向决策模块提供关于可能违反公平的反馈，以及提供公平认证。FairGame框架在概念上类似于强化学习中众所周知的参与者-批评者方法；但是，与参与者-批评者方法不同，它强制审计者只有对策略的查询访问权限，相反，决策模块只能查询审计者(提供决策反馈)。不同的公平和效率概念可以被建模为决策模块和审计师之间不同类型的两人博弈。本项目将研究这一框架中的基本问题，包括：(A)在黑箱环境中(概率地)证明公平的可能性有多大；(B)审计的实用算法；(C)在获得审计人员的黑箱的情况下，确保公平决策的迭代办法，包括政策梯度方法和贝叶斯优化；(D)适当的公平和效力标准；以及(E)这些标准是否能满足不同的监管模式，例如“关于逻辑的有意义的信息”的要求或法律规定的不歧视要求。这项工作将受到为无家可归者家庭提供服务开发公平算法的真正政策挑战的启发，并在这一领域向关键利益相关者提供反馈。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 increasing impact of AI technologies on real applications has subjected these to unprecedented scrutiny. One of the major concerns is the extent to which these technologies reproduce or exacerbate inequity, with a number of high-profile examples, such as bias in recidivism prediction, illustrating the potential limitations of, and eroding trust in, AI. While approaches have emerged that aim to guarantee some form of fairness of AI systems, most are restricted to relatively simple prediction problems, without accounting for specific use cases of predictions. However, many practical uses of predictive models involve decisions that occur over time, and that are obtained by solving complex optimization problems. Moreover, few general approaches exist even for ascertaining equitable outcomes of dynamic decisions, let alone providing guidance for ensuring equity in such settings. To address these limitations, this project is developing a framework called FairGame for the development and certification of fair autonomous decision-making algorithms. This project will also develop new courses and course modules at Washington University, take a lead role in a new interdisciplinary program in Computational and Data Sciences, seek to inform policymakers and regulators about computational approaches to ensuring fairness, and work to broaden participation in computing through, for example, the Missouri Louis Stokes Alliance for Minority Participation.This project develops an audit-driven game theoretic framework for the development and certification of fair autonomous decision-making algorithms. FairGame features a decision module that computes a decision policy, and a pseudo-adversarial auditor providing feedback to the decision module about possible fairness violations, as well as providing fairness certification. The FairGame framework conceptually resembles the well-known actor-critic methods in reinforcement learning; however, unlike actor-critic methods, it enforces that the auditor has only query access to the policy, and, conversely, the decision module can only query the auditor (which provides feedback on the decisions). Different notions of fairness and efficacy can be modeled as different types of two-player games between the decision module and the auditor. This project will study foundational issues in this framework, including (a) the extent to which (probabilistically) certifying fairness in a black-box setting is possible, (b) practical algorithms for auditing, (c) iterative approaches for ensuring fair-decisions given a black-box access to an auditor, including policy gradient methods and Bayesian optimization, and (d) appropriate fairness and efficacy criteria, and (e) whether these criteria can satisfy different regulatory models, such as a requirement of “meaningful information about the logic” or legally imposed requirements of nondiscrimination. The work will be informed by the real policy challenge of developing fair algorithms for provision of services to homeless households, and provide feedback in this domain to key stakeholders.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.