Counterfactual Fairness

TLDR

Machine learning used in high‑stakes decisions can perpetuate past biases, so models must account for historical unfairness to avoid discriminatory outcomes. The authors propose a causal‑inference framework to model fairness. Counterfactual fairness requires that a prediction remain unchanged when the individual's demographic group is altered in a counterfactual scenario. Applying the framework to law‑school admission data, the authors show it can produce fair predictions of success.

Abstract

Machine learning can impact people with legal or ethical consequences when it is used to automate decisions in areas such as insurance, lending, hiring, and predictive policing. In many of these scenarios, previous decisions have been made that are unfairly biased against certain subpopulations, for example those of a particular race, gender, or sexual orientation. Since this past data may be biased, machine learning predictors must account for this to avoid perpetuating or creating discriminatory practices. In this paper, we develop a framework for modeling fairness using tools from causal inference. Our definition of counterfactual fairness captures the intuition that a decision is fair towards an individual if it is the same in (a) the actual world and (b) a counterfactual world where the individual belonged to a different demographic group. We demonstrate our framework on a real-world problem of fair prediction of success in law school.