Path-Specific Counterfactual Fairness

TL;DR

This paper introduces a causal method for fair decision-making that isolates and disregards unfair pathways influenced by sensitive attributes, improving fairness without complex computations.

Contribution

It presents a novel causal approach to path-specific fairness that simplifies previous methods and is applicable to complex, non-linear scenarios using deep learning.

Findings

Effective correction of biased observations

Applicable to complex, non-linear models

Avoids intractable path-specific effect computations

Abstract

We consider the problem of learning fair decision systems in complex scenarios in which a sensitive attribute might affect the decision along both fair and unfair pathways. We introduce a causal approach to disregard effects along unfair pathways that simplifies and generalizes previous literature. Our method corrects observations adversely affected by the sensitive attribute, and uses these to form a decision. This avoids disregarding fair information, and does not require an often intractable computation of the path-specific effect. We leverage recent developments in deep learning and approximate inference to achieve a solution that is widely applicable to complex, non-linear scenarios.