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

arXiv: https://arxiv.org/abs/2205.04460 · HTML: https://mlsanity.com/paper/2205.04460/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 115 references that are in the map.
- First posted on arXiv 6 May 2022.

## Builds on (most cited first, 27 of 27)

- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Language (Technology) is Power: A Critical Survey of "Bias" in NLP](https://mlsanity.com/paper/2005.14050/) (2020)
- [Inherent Trade-Offs in the Fair Determination of Risk Scores](https://mlsanity.com/paper/1609.05807/) (2016)
- [Fairness in Machine Learning: A Survey](https://mlsanity.com/paper/2010.04053/) (2020)
- [Fairness in Machine Learning: Lessons from Political Philosophy](https://mlsanity.com/paper/1712.03586/) (2017)
- [An Intersectional Definition of Fairness](https://mlsanity.com/paper/1807.08362/) (2018)
- [Towards a Critical Race Methodology in Algorithmic Fairness](https://mlsanity.com/paper/1912.03593/) (2019)
- [Lessons from Archives: Strategies for Collecting Sociocultural Data in Machine Learning](https://mlsanity.com/paper/1912.10389/) (2019)
- [Participation is not a Design Fix for Machine Learning](https://mlsanity.com/paper/2007.02423/) (2020)
- [Re-imagining Algorithmic Fairness in India and Beyond](https://mlsanity.com/paper/2101.09995/) (2021)
- [50 Years of Test (Un)fairness: Lessons for Machine Learning](https://mlsanity.com/paper/1811.10104/) (2018)
- [Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligence](https://mlsanity.com/paper/2007.04068/) (2020)
- [Problem Formulation and Fairness](https://mlsanity.com/paper/1901.02547/) (2019)
- [Roles for Computing in Social Change](https://mlsanity.com/paper/1912.04883/) (2019)
- [Racial categories in machine learning](https://mlsanity.com/paper/1811.11668/) (2018)
- [Bringing the People Back In: Contesting Benchmark Machine Learning Datasets](https://mlsanity.com/paper/2007.07399/) (2020)
- [Algorithmic Fairness from a Non-ideal Perspective](https://mlsanity.com/paper/2001.09773/) (2020)
- [A Hierarchy of Limitations in Machine Learning](https://mlsanity.com/paper/2002.05193/) (2020)
- [Towards decolonising computational sciences](https://mlsanity.com/paper/2009.14258/) (2020)
- [Interventions over Predictions: Reframing the Ethical Debate for Actuarial Risk Assessment](https://mlsanity.com/paper/1712.08238/) (2017)
- [What If I Don't Like Any Of The Choices? The Limits of Preference Elicitation for Participatory Algorithm Design](https://mlsanity.com/paper/2007.06718/) (2020)
- [Black Feminist Musings on Algorithmic Oppression](https://mlsanity.com/paper/2101.09869/) (2021)
- [Fawkes: Protecting Privacy against Unauthorized Deep Learning Models](https://mlsanity.com/paper/2002.08327/) (2020)
- [Biased Programmers? Or Biased Data? A Field Experiment in Operationalizing AI Ethics](https://mlsanity.com/paper/2012.02394/) (2020)
- [Combating Anti-Blackness in the AI Community](https://mlsanity.com/paper/2006.16879/) (2020)
- [Questioning the assumptions behind fairness solutions](https://mlsanity.com/paper/1811.11293/) (2018)

## Built on by (most cited first, 25 of 25)

- [AI's Regimes of Representation: A Community-centered Study of Text-to-Image Models in South Asia](https://mlsanity.com/paper/2305.11844/) (2023)
- [Queer In AI: A Case Study in Community-Led Participatory AI](https://mlsanity.com/paper/2303.16972/) (2023)
- [Fairness and Bias in Multimodal AI: A Survey](https://mlsanity.com/paper/2406.19097/) (2024)
- ["I Am the One and Only, Your Cyber BFF": Understanding the Impact of GenAI Requires Understanding the Impact of Anthropomorphic AI](https://mlsanity.com/paper/2410.08526/) (2024)
- [Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond](https://mlsanity.com/paper/2309.00064/) (2023)
- [Flickr Africa: Examining Geo-Diversity in Large-Scale, Human-Centric Visual Data](https://mlsanity.com/paper/2308.08656/) (2023)
- [Fairness Hacking: The Malicious Practice of Shrouding Unfairness in Algorithms](https://mlsanity.com/paper/2311.06826/) (2023)
- [Undesirable Biases in NLP: Addressing Challenges of Measurement](https://mlsanity.com/paper/2211.13709/) (2022)
- [Manifestations of Xenophobia in AI Systems](https://mlsanity.com/paper/2212.07877/) (2022)
- [Understanding "Democratization" in NLP and ML Research](https://mlsanity.com/paper/2406.11598/) (2024)
- [Interactive Text Generation](https://mlsanity.com/paper/2303.00908/) (2023)
- [Machine Learning practices and infrastructures](https://mlsanity.com/paper/2307.06518/) (2023)
- [Articulation Work and Tinkering for Fairness in Machine Learning](https://mlsanity.com/paper/2407.16496/) (2024)
- [Data Defenses Against Large Language Models](https://mlsanity.com/paper/2410.13138/) (2024)
- [Bias Mitigation Methods for Binary Classification Decision-Making Systems: Survey and Recommendations](https://mlsanity.com/paper/2305.20020/) (2023)
- [Identifying Reasons for Bias: An Argumentation-Based Approach](https://mlsanity.com/paper/2310.16506/) (2023)
- [Modeling Fairness in Recruitment AI via Information Flow](https://mlsanity.com/paper/2511.13793/) (2025)
- [Evaluating AI Group Fairness: a Fuzzy Logic Perspective](https://mlsanity.com/paper/2406.18939/) (2024)
- [The Return of Pseudosciences in Artificial Intelligence: Have Machine Learning and Deep Learning Forgotten Lessons from Statistics and History?](https://mlsanity.com/paper/2411.18656/) (2024)
- [Laypeople's Attitudes Towards Fair, Affirmative, and Discriminatory Decision-Making Algorithms](https://mlsanity.com/paper/2505.07339/) (2025)
- [Variable Selection in the Context of AI Fairness](https://mlsanity.com/paper/2608.11251/) (2026)
- [Rules Before Oracles: Auditable, User-Configurable Argument Selection for Deliberative Polling](https://mlsanity.com/paper/2608.23979/) (2026)
- [The Gold in Bias: Maturing the AI Design Process through Verification](https://mlsanity.com/paper/2609.29730/) (2026)
- [Transparency as Answerability: Role-Dependent Information Flow Modeling](https://mlsanity.com/paper/2610.16887/) (2026)
- [Transparent Information Flow Modeling for Role-Aware AI Act Assessment](https://mlsanity.com/paper/2610.16916/) (2026)

## Abstract (arXiv)

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 major themes of critique wherein these different fields intersect: 1) how "fairness" in AI fairness research gets defined; 2) how problems for AI systems to address get formulated; 3) the impacts of abstraction on how AI tools function and its propensity to lead to technological solutionism; 4) how racial classification operates within AI fairness research; 5) the use of AI fairness measures to avoid regulation and engage in ethics washing; 6) an absence of participatory design and democratic deliberation in AI fairness considerations; 7) data collection practices that entrench "bias," are non-consensual, and lack transparency; 8) the predatory inclusion of marginalized groups into AI systems; and 9) a lack of engagement with AI's long-term social and ethical outcomes. Drawing from these critiques, the article concludes by imagining future ML fairness research directions that actively disrupt entrenched power dynamics and structural injustices in society.