CIF: Small: Foundations and Applications of Blind Subgroup Robustness

CIF：小：盲子群鲁棒性的基础和应用

• 批准号: 2120018
• 负责人: Guillermo Sapiro
• 金额: $ 45.11万
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2120018&HistoricalAwards=false
• 关键词: CIF; Small; Foundations; Applications; Blind

Machine-learning algorithms may present discriminatory behavior across certain subgroups, meaning that segments of the overall population are measurably under-served by the model, rendering the decisions unfair. The most common approaches to address this challenge consider that the algorithm has access to a set of predefined protected subgroups during training, and the goal is to learn a model that satisfies a certain notion of fairness/robustness across these subgroups. Perfect fairness can, in general, only be achieved by degrading the performance of the benefited subgroups without necessarily improving the disadvantaged and protected ones. This conflicts with ethical and legal notions of no-harm fairness, which are appropriate where quality of service is paramount, for example in health. To address this, this work considers notions of fairness and subgroup robustness that guarantee no unnecessary harm is done to any subgroup. The project goes beyond this since it considers the case where the subgroups or demographics are not known a priori and might even change with time and algorithm deployment. The project brings these concepts of blind and no-harm subgroup robustness and fairness to the area of backwards compatibility, where the goal is to guarantee that new machine-learning algorithms are compatible with previous ones; and to the area of federated learning, where multiple sites share data for the sake of mutual benefit. Lastly, potential connections of the proposed blind and no unnecessary-harm subgroup robustness with causal inference are investigated. The project first formally studies blind and no-unnecessary-harm (Pareto optimal) subgroup robustness, where the machine-learning algorithm needs to be robust to all possible subgroups of the data (given a minimal subgroup size), without necessarily knowing in advance the subgroups' defining characteristics. This is formally studied, including the tradeoffs and costs of protecting unknown subgroups and the corresponding optimization algorithm; concepts of data and optimization uncertainty are also included to model potential sacrifices a subgroup can make in benefit of others. Such formal study of blind subgroup robustness is an emerging field in the machine-learning community, and this project provides a fundamental and unifying view of it, combining theory with practice and critical information for policy makers. The project then extends the work to the area of backwards compatibility, with the goal to make all potential subgroups equally backwards compatible; and to federated learning, where the subgroup fairness and robustness is considered both across the silos/participants and inside each silo itself. Finally, thanks to the close mathematical connection between invariant features and causality, the project further considers this proposed unifying framework of blind subgroup robustness to study connections between the automatically discovered critical subgroups, their features, and causality. Health applications provide a unique testbed for the frameworks developed here.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.

机器学习算法可能会在某些子群体中表现出歧视性行为，这意味着总体人口的一部分明显没有得到模型的服务，从而导致决策不公平。解决这一挑战的最常见方法是考虑算法在训练期间可以访问一组预定义的受保护子组，目标是学习一个模型，该模型满足这些子组之间的某种公平/鲁棒性概念。一般来说，完美的公平只能通过降低受益群体的表现来实现，而不一定改善弱势群体和受保护群体。这与无伤害公平的道德和法律观念相冲突，这种观念适用于服务质量至关重要的领域，例如卫生领域。为了解决这个问题，本工作考虑了公平性和子群鲁棒性的概念，保证不对任何子群造成不必要的伤害。该项目超越了这一点，因为它考虑了子群体或人口统计数据不是先验的，甚至可能随着时间和算法部署而改变的情况。该项目将这些盲和无伤害子组鲁棒性和公平性的概念引入到向后兼容性领域，其目标是保证新的机器学习算法与以前的算法兼容；在联合学习领域，多个站点为了互惠互利共享数据。最后，研究了所提出的盲和无不必要伤害亚组鲁棒性与因果推理的潜在联系。该项目首先正式研究盲和无不必要伤害（帕累托最优）子群鲁棒性，其中机器学习算法需要对数据的所有可能子组（给定最小子组大小）具有鲁棒性，而不必事先知道子组的定义特征。正式研究了这一问题，包括保护未知子群的权衡和代价以及相应的优化算法；数据和优化不确定性的概念也被纳入模型的潜在牺牲，一个子群体可以为他人的利益。这种对盲子群鲁棒性的正式研究是机器学习社区的一个新兴领域，该项目提供了一个基本的和统一的观点，将理论与实践结合起来，为政策制定者提供了关键信息。然后，该项目将工作扩展到向后兼容性领域，目标是使所有潜在的子组同样向后兼容；在联邦学习中，子组的公平性和鲁棒性既要跨筒仓/参与者考虑，也要在每个筒仓内部考虑。最后，由于不变特征与因果关系之间的密切数学联系，该项目进一步考虑提出的盲子群鲁棒性统一框架，以研究自动发现的关键子群及其特征与因果关系之间的联系。健康应用程序为这里开发的框架提供了一个独特的测试平台。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。

项目成果

Minimax Pareto Fairness: A Multi Objective Perspective

• 发表时间: 2020-07
• 期刊: Proceedings of machine learning research
• 作者: Natalia Martínez;Martín Bertrán;G. Sapiro

Using text to teach image retrieval

使用文本教授图像检索

• DOI: 10.1109/cvprw53098.2021.00180
• 发表时间: 2021
• 期刊: CVPR 2021 Workshop
• 作者: H. Dong, Z. Wang

Robust Hybrid Learning With Expert Augmentation

• 发表时间: 2022-02
• 期刊: Trans. Mach. Learn. Res.
• 作者: Antoine Wehenkel;Jens Behrmann;Hsiang Hsu;G. Sapiro;Gilles Louppe and;J. Jacobsen

Minimax Demographic Group Fairness in Federated Learning

• DOI: 10.1145/3531146.3533081
• 发表时间: 2022-01
• 期刊: Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency
• 作者: Afroditi Papadaki;Natalia Martínez;Martín Bertrán;G. Sapiro;Miguel R. D. Rodrigues

Instance based Generalization in Reinforcement Learning

• 发表时间: 2020-11
• 期刊: ArXiv
• 作者: Martín Bertrán;Natalia Martínez;Mariano Phielipp;G. Sapiro