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III: Towards Causal Fair Decision-making

III：走向因果公平决策

基本信息

批准号：
2040971
负责人：
Elias Bareinboim
金额：
$ 73.95万
依托单位：
Columbia 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：
2021-05-15 至 2024-04-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2040971&HistoricalAwards=false
关键词：
III Towards Causal Fair Decision

项目摘要

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.

人工智能（AI）在现代社会中扮演着越来越重要的角色，因为曾经由人类做出的决策现在被委托给自动化系统。这些系统目前负责决定银行贷款、罪犯的监禁和新员工的雇佣，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。尽管这一任务涉及重大利益，但人们仍然几乎不了解这种制度的一些基本性质，包括公平和透明度问题。例如，有大量的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认为是公平的指标仍然是一项难以捉摸，几乎令人生畏的任务。此外，这些指标几乎总是以任意的方式进行，没有太多的理由或理由。在这个项目中，我们将开发数学基础，用于（1）帮助数据科学家分析已经部署的决策系统中存在的不公平性和（可能）不公平性的“大小”，以及（2）指导系统设计人员在确定既定的公平性和准确性水平的同时，在他们将要部署的系统中选择公平性标准。该建议旨在为实现因果公平决策的目标做出基础和方法上的贡献。在基础层面上，我们建立在因果关系理论的基础上，以引出正式理解公平问题所必需的原则，公平问题与数据背后的真正因果机制交织在一起。特别是，我们研究了文献中的各种公平性措施，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，我们的目标是弥合因果分析和可扩展的机器学习方法之间的差距，通过新的想法，有效的估计，预测和优化因果公平措施。这包括加权经验风险最小化方法，用于从离线数据中估计因果公平性措施，主动学习和探索技术，（离线和在线）学习，鲁棒的优化方法来处理模型误指定，和强化学习技术，以了解公平/该奖项反映了NSF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。