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

Abstract

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.

35 pages

References in corpus (4)

• Towards a Critical Race Methodology in Algorithmic Fairness
• Bringing the People Back In: Contesting Benchmark Machine Learning Datasets
• A Hierarchy of Limitations in Machine Learning
• What If I Don't Like Any Of The Choices? The Limits of Preference Elicitation for Participatory Algorithm Design

Cited by in corpus (10)

• Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond
• AI's Regimes of Representation: A Community-centered Study of Text-to-Image Models in South Asia
• Queer In AI: A Case Study in Community-Led Participatory AI
• Macro Ethics Principles for Responsible AI Systems: Taxonomy and Future Directions
• Fairness Hacking: The Malicious Practice of Shrouding Unfairness in Algorithms
• Machine Learning practices and infrastructures
• Undesirable Biases in NLP: Addressing Challenges of Measurement
• An intersectional framework for counterfactual fairness in risk prediction
• Flickr Africa: Examining Geo-Diversity in Large-Scale, Human-Centric Visual Data
• Bridging Research and Practice Through Conversation: Reflecting on Our Experience