III: Towards Causal Fair Decision-making

人工智能(AI)在现代社会中发挥着越来越重要的作用，因为曾经由人类做出的决定现在正被委托给自动化系统。这些系统目前负责决定银行贷款、监禁罪犯和雇用新员工，不难想象未来人工智能将支撑社会的大部分决策基础设施。尽管这项任务涉及的风险很高，但对这种制度的一些基本性质，包括公平和透明度问题，几乎仍然一无所知。例如，有许多标准和方法试图解释决策中的不公平，但选择人工智能系统必须遵守的被视为公平的指标仍然是一项难以捉摸、几乎令人望而生畏的任务。此外，这些指标几乎总是以任意的方式执行，没有太多的理由或理由。在这个项目中，我们将为(1)帮助数据科学家分析已经部署的决策系统中不公平的存在和(可能)不公平的程度，以及(2)指导系统的设计者在他们要部署的系统中选择公平标准的过程中，同时确定已建立的公平和准确性水平，开发数学基础。该提案旨在为实现因果公平决策的目标作出基础性和方法性的贡献。在基本层面上，我们建立在因果关系理论的基础上，得出正式理解公平问题所需的原则，公平问题与数据背后的真正因果机制交织在一起。特别是，我们研究了文献中可用的各种公平衡量标准，以及它们相对于未观察到的因果机制的检测和解释能力。在方法论方面，我们的目标是通过在因果公平度量下的有效估计、预测和优化的新想法来弥合因果分析和可伸缩机器学习方法之间的差距。这包括用于从离线数据估计因果公平度量的加权经验风险最小化方法，用于混合(离线和在线)学习的主动学习和探索技术，用于处理模型错误指定的稳健优化方法，以及用于理解公平/不公平政策的长期影响的强化学习技术。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。

Artificial Intelligence (AI) plays an increasingly prominent role in modern society because decisions that were once made by humans are now being delegated to automated systems. These systems are currently in charge of deciding bank loans, criminals' incarceration, and the hiring of new employees, and it is not difficult to envision a future where AI will underpin most of the society's decision-making infrastructure. Despite the high stakes entailed by this task, there is still almost no understanding of some basic properties of such systems, including issues of fairness and transparency. For instance, there is a proliferation of criteria and methods trying to account for unfairness in decision-making, but choosing a metric that the AI system must adhere to be deemed fair remains an elusive, almost daunting task. Also, these metrics are almost invariably carried out in an arbitrary fashion, without much justification or rationale. In this project, we will develop the mathematical foundations for (1) assisting data scientists analyzing the existence and (possibly) the `magnitude' of unfairness in an already deployed decision-system and (2) guiding system's designers in the process of selecting a fairness criterion in their to-be-deployed system while ascertaining an established level of fairness and accuracy. This proposal aims to make both foundational and methodological contributions towards the goal of causal fair decision-making. At a foundational level, we build on causality theory to elicit the principles necessary to formally understand the problem of fairness, which is intertwined with the true causal mechanisms underlying the data. In particular, we study various measures of fairness available in the literature and their detection and explanatory power relative to the unobserved causal mechanisms. On the methodological side, we aim to bridge the gap between causal analysis and scalable machine learning methods through novel ideas for efficient estimation, prediction, and optimization under causal fairness measures. This includes weighted empirical risk minimization methods for estimating causal fairness measures from offline data, active learning and exploration techniques for hybrid (offline and online) learning, robust optimization methods to handle model misspecification, and reinforcement learning techniques for understanding long-term impact of fair/unfair policies.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.