Dancing in the Shadows: Harnessing Ambiguity for Fairer Classifiers

TL;DR

This paper proposes a novel fairness-enhancing method that leverages ambiguous instances with uncertain sensitive attributes to train classifiers, improving fairness in real-world scenarios where sensitive information is incomplete.

Contribution

It introduces a new approach that uses ambiguous data points to enhance fairness guarantees in machine learning classifiers.

Findings

Enhanced fairness in classifier predictions.

Leveraging ambiguous instances improves fairness metrics.

Potential for better fairness in real-world applications.

Abstract

This paper introduces a novel approach to bolster algorithmic fairness in scenarios where sensitive information is only partially known. In particular, we propose to leverage instances with uncertain identity with regards to the sensitive attribute to train a conventional machine learning classifier. The enhanced fairness observed in the final predictions of this classifier highlights the promising potential of prioritizing ambiguity (i.e., non-normativity) as a means to improve fairness guarantees in real-world classification tasks.