On Learning and Testing of Counterfactual Fairness through Data Preprocessing

TL;DR

This paper introduces the FLAP algorithm for learning counterfactually fair decisions through data preprocessing, formalizes conditions for fairness, and demonstrates its effectiveness on simulated and real data.

Contribution

It develops a novel data preprocessing method to achieve counterfactual fairness and formalizes the conditions under which it guarantees fairness.

Findings

FLAP effectively learns fair decisions from biased data.

Counterfactual fairness is shown to be equivalent to conditional independence.

The method detects discrimination using processed data.

Abstract

Machine learning has become more important in real-life decision-making but people are concerned about the ethical problems it may bring when used improperly. Recent work brings the discussion of machine learning fairness into the causal framework and elaborates on the concept of Counterfactual Fairness. In this paper, we develop the Fair Learning through dAta Preprocessing (FLAP) algorithm to learn counterfactually fair decisions from biased training data and formalize the conditions where different data preprocessing procedures should be used to guarantee counterfactual fairness. We also show that Counterfactual Fairness is equivalent to the conditional independence of the decisions and the sensitive attributes given the processed non-sensitive attributes, which enables us to detect discrimination in the original decision using the processed data. The performance of our algorithm is illustrated using simulated data and real-world applications.