Rethinking Fairness: An Interdisciplinary Survey of Critiques of Hegemonic ML Fairness Approaches

By Lindsay Weinberg et al

Published on May 10, 2022

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Table of Contents

1. Introduction
2. Article Selection
3. Major Themes of Critique

Summary

This survey article assesses and compares existing critiques of current fairness-enhancing technical interventions in machine learning (ML) that draw from a range of non-computing disciplines. It bridges epistemic divides to offer an interdisciplinary understanding of the possibilities and limits of hegemonic computational approaches to ML fairness. The article is organized according to nine major themes of critique, exploring issues such as how 'fairness' in AI fairness research gets defined, impacts of abstraction on AI tools, racial classification, data collection practices, and more. The author aims to broaden approaches for disrupting entrenched power dynamics in ML fairness research.