Rethinking Fairness: An Interdisciplinary Survey of Critiques of Hegemonic ML Fairness Approaches. (arXiv:2205.04460v1 [cs.LG])

This survey article assesses and compares existing critiques of current
fairness-enhancing technical interventions into machine learning (ML) that draw
from a range of non-computing disciplines, including philosophy, feminist
studies, critical race and ethnic studies, legal studies, anthropology, and
science and technology studies. It bridges epistemic divides in order to offer
an interdisciplinary understanding of the possibilities and limits of hegemonic
computational approaches to ML fairness for producing just outcomes for
society's most marginalized. The article is organized according to nine …

arxiv fairness ml survey