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定已被委派给自动系统。这些系统目前负责决定银行贷款，罪犯的监禁和雇用新员工，并且不难设想AI将为社会决策基础设施大部分基础的未来。尽管这项任务需要高赌注，但几乎仍然对此类系统的某些基本属性（包括公平和透明度问题）几乎没有理解。例如，试图解释决策中不公平性的标准和方法的扩散，但是选择一个指标，即AI系统必须遵守被视为公平仍然是一项难以捉摸的，几乎令人生畏的任务。而且，这些指标几乎总是以任意方式进行，没有太多理由或理由。在这个项目中，我们将开发（1）协助数据科学家在已经部署的决策系统中分析不公平的存在以及（可能）（可能）（可能）（2）指导系统的设计师在选择公平系统中的公平标准的过程中指导系统的设计师，同时确定公平和确定的公平性和准确性。该提案旨在为因果公平决策的目标做出基础和方法上的贡献。在基础上，我们以因果关系理论为基础，以引发正式理解公平问题所必需的原则，这与数据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度量及其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，我们旨在通过新颖的思想来弥合因果分析和可扩展的机器学习方法之间的差距，以在因果公平度量下进行有效的估计，预测和优化。这包括加权经验风险最小化方法，用于估算离线数据，主动学习和探索技术的因果公平措施（离线和在线学习），可靠的优化方法来处理模型指定的模型，并加强了误解的学习技术，以理解公平/不公平奖的长期影响。和更广泛的影响审查标准。