III: Towards Causal Fair Decision-making

III：走向因果公平决策

• 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

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) 在现代社会中发挥着越来越重要的作用，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、监禁罪犯和雇用新员工，不难想象未来人工智能将支撑大部分社会决策基础设施。尽管这项任务的风险很高，但人们仍然几乎不了解此类系统的一些基本属性，包括公平性和透明度问题。例如，试图解释决策中的不公平性的标准和方法不断涌现，但选择人工智能系统必须遵守的衡量标准仍然是一项难以捉摸、几乎令人畏惧的任务。此外，这些指标几乎总是以任意方式执行，没有太多理由或理由。 在这个项目中，我们将开发数学基础：(1) 协助数据科学家分析已部署的决策系统中不公平现象的存在和（可能）“程度”；(2) 指导系统设计者选择一个决策系统。其将要部署的系统中的公平标准，同时确定既定的公平性和准确性水平。该提案旨在为实现因果公平决策的目标做出基础和方法论贡献。在基础层面上，我们以因果关系理论为基础，引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性衡量标准及其相对于未观察到的因果机制的检测和解释力。 在方法论方面，我们的目标是通过在因果公平性措施下有效估计、预测和优化的新思想来弥合因果分析和可扩展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性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、处理模型错误指定的稳健优化方法以及用于理解公平的长期影响的强化学习技术。 /不公平的政策。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点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

Double Machine Learning Density Estimation for Local Treatment Effects with Instruments

使用仪器进行局部治疗效果的双重机器学习密度估计

• 发表时间: 2021-01
• 期刊: Advances in neural information processing systems
• 作者: Jung, Y.;Tian, J.;Bareinboim, E.
• 通讯作者: Bareinboim, E.

Estimating Identifiable Causal Effects on Markov Equivalence Class through Double Machine Learning

通过双机器学习估计马尔可夫等价类的可识别因果效应

• 发表时间: 2021-01
• 期刊: Proceedings of Machine Learning Research
• 作者: Jung, Yonghan;Tian, Jin;Bareinboim, Elias
• 通讯作者: Bareinboim, Elias