Title: Fairness in representation: quantifying stereotyping as a representational harm,
Abstract: While harms of allocation have been increasingly studied as part of the
subfield of algorithmic fairness, harms of representation have received
considerably less attention. In this paper, we formalize two notions of
stereotyping and show how they manifest in later allocative harms within the
machine learning pipeline. We also propose mitigation strategies and
demonstrate their effectiveness on synthetic datasets.