EAGER: Causal Bayesian Network-Based Discrimination Discovery and Prevention

EAGER：基于因果贝叶斯网络的歧视发现和预防

• 批准号: 1646654
• 负责人: Xintao Wu
• 金额: $ 20万
• 依托单位: University of Arkansas
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1646654&HistoricalAwards=false
• 关键词: EAGER; Causal; Bayesian; Network; Based

Various business models have been built around the collection and use of customer data to make important decisions like employment, credit, and insurance. There are increasing worries of discrimination as data analytics technologies could be used to unfairly treat individuals based on their demographic information such as gender, age, marital status, race, religion or belief, membership in a national minority, disability, or illness. It is imperative to develop predictive decision models, such that the data that goes into them and the decisions made with their assistance are not subject to discrimination. This EAGER research designs practical techniques to accurately detect and remove discrimination from the datasets used to build decision models. A primary outcome of this research is a unifying framework and a prototype system for discrimination discovery and removal. This system can help individuals from disadvantaged groups determine whether they are fairly treated and help decision makers from organizations ensure their predictive decision models are discrimination free. Existing discrimination discovery approaches are mainly based on correlation or association and cannot accurately discover the true discrimination. In addition, each of them targets on one or two types of discrimination only. This research categorizes discrimination based on whether discrimination is across the whole system, occurs in one subsystem, or happens to one individual, and whether discrimination is a direct effect or an indirect effect on the decision. This research then develops a unifying causal Bayesian network based framework that takes into consideration the distinctions between discrimination and general causalities and models both direct discrimination and indirect discrimination as causal effects via different paths between protected attributes and the decision. It can accurately capture and measure various types of discrimination at system, group, and individual levels. The research then develops novel discrimination discovery and prevention models and algorithms. The research also builds a testing framework for simulating different types of discrimination and evaluating the approaches based on various metrics, and integrates the discrimination discovery and prevention algorithms into an open source data mining and machine learning software system.

围绕客户数据的收集和使用，已经建立了各种业务模型，以做出就业、信贷和保险等重要决策。由于数据分析技术可能被用来根据个人的人口统计信息（如性别、年龄、婚姻状况、种族、宗教或信仰、少数民族成员、残疾或疾病）不公平地对待个人，因此对歧视的担忧日益增加。必须开发预测性决策模型，使进入模型的数据和在模型的帮助下做出的决定不受歧视。这项EAGER研究设计了实用的技术来准确地检测和消除用于构建决策模型的数据集中的歧视。本研究的主要成果是一个统一的框架和原型系统的歧视发现和消除。该系统可以帮助弱势群体的个人确定他们是否受到公平对待，并帮助组织的决策者确保他们的预测决策模型不受歧视。现有的歧视发现方法主要基于相关性或关联，不能准确发现真正的歧视。此外，每一项都只针对一种或两种歧视。本研究根据歧视是贯穿整个系统，发生在一个子系统，还是发生在一个个体，以及歧视是对决策的直接影响还是间接影响来对歧视进行分类。然后，本研究开发了一个统一的基于因果贝叶斯网络的框架，该框架考虑了歧视和一般因果关系之间的区别，并通过保护属性和决策之间的不同路径将直接歧视和间接歧视作为因果效应建模。它可以准确地捕捉和衡量系统、群体和个人层面的各种歧视。然后，该研究开发了新的歧视发现和预防模型和算法。该研究还构建了一个测试框架，用于模拟不同类型的歧视，并基于各种指标评估方法，并将歧视发现和预防算法集成到开源数据挖掘和机器学习软件系统中。

项目成果

Anti-discrimination learning: a causal modeling-based framework

反歧视学习：基于因果建模的框架

• DOI: 10.1007/s41060-017-0058-x
• 发表时间: 2017
• 期刊: International Journal of Data Science and Analytics
• 影响因子: 2.4
• 作者: Zhang, Lu;Wu, Xintao
• 通讯作者: Wu, Xintao

A Generative Adversarial Framework for Bounding Confounded Causal Effects

• DOI: 10.1609/aaai.v35i13.17437
• 发表时间: 2021-05
• 作者: Yaowei Hu;Yongkai Wu;Lu Zhang;Xintao Wu

A Causal Framework for Discovering and Removing Direct and Indirect Discrimination

发现和消除直接和间接歧视的因果框架

• DOI: 10.24963/ijcai.2017/549
• 发表时间: 2017
• 期刊: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
• 作者: Zhang, Lu;Wu, Yongkai;Wu, Xintao
• 通讯作者: Wu, Xintao

Using Loglinear Model for Discrimination Discovery and Prevention

使用对数线性模型进行歧视发现和预防

• DOI: 10.1109/dsaa.2016.18
• 发表时间: 2016
• 期刊: 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA
• 作者: Wu, Yongkai;Wu, Xintao
• 通讯作者: Wu, Xintao

Causal Modeling-Based Discrimination Discovery and Removal: Criteria, Bounds, and Algorithms

基于因果模型的歧视发现和消除：标准、界限和算法

• DOI: 10.1109/tkde.2018.2872988
• 发表时间: 2019
• 期刊: IEEE Transactions on Knowledge and Data Engineering
• 影响因子: 8.9
• 作者: Zhang, Lu;Wu, Yongkai;Wu, Xintao
• 通讯作者: Wu, Xintao