Entropy, Vol. 23, Pages 1571: Interventional Fairness with Indirect Knowledge of Unobserved Protected Attributes (Entropy)

The deployment of machine learning (ML) systems in applications with societal impact has motivated the study of fairness for marginalized groups. Often, the protected attribute is absent from the training dataset for legal reasons. However, datasets still contain proxy attributes that capture protected information and can inject unfairness in the ML model. Some deployed systems allow auditors, decision makers, or affected users to report issues or seek recourse by flagging individual samples. In this work, we examined such systems and considered a feedback-based framework where the protected attribute is unavailable and the flagged samples are indirect knowledge. The reported samples were used as guidance to identify the proxy attributes that are causally dependent on the (unknown) protected attribute. We worked under the causal interventional fairness paradigm. Without requiring the underlying structural causal model a priori, we propose an approach that performs conditional independence tests on observed data to identify such proxy attributes. We theoretically proved the optimality of our algorithm, bound its complexity, and complemented it with an empirical evaluation demonstrating its efficacy on various real-world and synthetic datasets.