EAGER: Causal Bayesian Network-Based Discrimination Discovery and Prevention

University Of Arkansas, Fayetteville AR

Investigators

Abstract

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.