Tuning Derivatives for Causal Fairness in Machine Learning

TL;DR

This paper introduces a new causal framework for fairness in machine learning with continuous protected attributes, proposing a tuning algorithm to balance statistical parity and predictive parity.

Contribution

It formalizes fairness criteria using path-specific derivatives, characterizes conditions for their compatibility, and develops a practical algorithm for fair predictor tuning.

Findings

The proposed method effectively balances SP and PP in experiments.

Formalization via derivatives extends fairness definitions to continuous attributes.

The tuning algorithm outperforms previous methods when considering predictive parity.

Abstract

Artificial-intelligence systems are becoming ubiquitous in society, yet their predictions typically inherit biases with respect to protected attributes such as race, gender, or age. Classical fairness notions, most notably Statistical Parity (SP), demand that predictions be independent of the protected attributes, but are overly restrictive when these attributes influence mediating variables that are considered business necessities. Recent causal formulations relax SP by distinguishing allowed from not-allowed causal paths and by complementing SP with Predictive Parity (PP), requiring the predictor to replicate the legitimate influence of business-necessities. Existing path-based definitions are mainly practical when applied to categorical attributes. This paper introduces a new framework for fairness in structural causal models that is tailored to continuous protected attributes. We formalize SP and PP through path-specific partial derivatives, establish conditions under which these criteria coincide with prior causal definitions, and characterize when a fair predictor, one that satisfies SP along not-allowed paths while achieving PP along allowed paths, exists. Building on this theory, we propose a fair tuning algorithm that either constructs such a predictor or, when not possible, allows for a trade-off between SP and PP. We present experiments on simulated and real data to evaluate our proposal, compare it with previously proposed methods, and show that it performs better when PP is considered.