Dancing in the Shadows: Harnessing Ambiguity for Fairer Classifiers
This addresses fairness issues in classification tasks for scenarios with ambiguous sensitive information, representing an incremental improvement.
The paper tackles algorithmic fairness when sensitive attributes are partially known by leveraging instances with uncertain identity to train a classifier, resulting in enhanced fairness in predictions.
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.