Exposing the Probabilistic Causal Structure of Discrimination

TL;DR

This paper introduces a causal inference method based on Suppes' probabilistic causation theory to detect various forms of discrimination in data, moving beyond correlation-based approaches.

Contribution

It develops a novel causal modeling framework called Suppes-Bayes Causal Network (SBCN) and a toolkit for analyzing discrimination legally and ethically.

Findings

Effective in detecting direct and indirect discrimination

Successfully applied to real-world datasets

Outperforms correlation-based methods in causal discrimination detection

Abstract

Discrimination discovery from data is an important task aiming at identifying patterns of illegal and unethical discriminatory activities against protected-by-law groups, e.g., ethnic minorities. While any legally-valid proof of discrimination requires evidence of causality, the state-of-the-art methods are essentially correlation-based, albeit, as it is well known, correlation does not imply causation. In this paper we take a principled causal approach to the data mining problem of discrimination detection in databases. Following Suppes' probabilistic causation theory, we define a method to extract, from a dataset of historical decision records, the causal structures existing among the attributes in the data. The result is a type of constrained Bayesian network, which we dub Suppes-Bayes Causal Network (SBCN). Next, we develop a toolkit of methods based on random walks on top of the SBCN, addressing different anti-discrimination legal concepts, such as direct and indirect discrimination, group and individual discrimination, genuine requirement, and favoritism. Our experiments on real-world datasets confirm the inferential power of our approach in all these different tasks.