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This survey article assesses and compares existing critiques of currentfairness-enhancing technical interventions into machine learning (ML) that drawfrom a range of non-computing disciplines, including philosophy, feministstudies, critical race and ethnic studies, legal studies, anthropology, andscience and technology studies. It bridges epistemic divides in order to offeran interdisciplinary understanding of the possibilities and limits of hegemoniccomputational approaches to ML fairness for producing just outcomes forsociety's most marginalized. The article is organized according to nine majorthemes of critique wherein these different fields intersect: 1) how "fairness"in AI fairness research gets defined; 2) how problems for AI systems to addressget formulated; 3) the impacts of abstraction on how AI tools function and itspropensity to lead to technological solutionism; 4) how racial classificationoperates within AI fairness research; 5) the use of AI fairness measures toavoid regulation and engage in ethics washing; 6) an absence of participatorydesign and democratic deliberation in AI fairness considerations; 7) datacollection practices that entrench "bias," are non-consensual, and lacktransparency; 8) the predatory inclusion of marginalized groups into AIsystems; and 9) a lack of engagement with AI's long-term social and ethicaloutcomes. Drawing from these critiques, the article concludes by imaginingfuture ML fairness research directions that actively disrupt entrenched powerdynamics and structural injustices in society.

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