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.
@article{arxiv.1802.08139,
title = {Path-Specific Counterfactual Fairness},
author = {Silvia Chiappa and Thomas P. S. Gillam},
journal= {arXiv preprint arXiv:1802.08139},
year = {2018}
}