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.
@article{arxiv.1901.09565,
title = {Fairness in representation: quantifying stereotyping as a representational harm},
author = {Mohsen Abbasi and Sorelle A. Friedler and Carlos Scheidegger and Suresh Venkatasubramanian},
journal= {arXiv preprint arXiv:1901.09565},
year = {2019}
}
Comments
9 pages, 6 figures, Siam International Conference on Data Mining