# What Is the Point of Equality in Machine Learning Fairness? Beyond Equality of Opportunity arXiv: https://arxiv.org/abs/2506.16782 ยท HTML: https://mlsanity.com/paper/2506.16782/ ## Key facts - Cited by 1 papers in the map (data through 14 Oct 2026). - Builds on 32 of its 98 references that are in the map. - First posted on arXiv 20 Jun 2025. ## Builds on (most cited first, 30 of 32) - [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019) - [Language (Technology) is Power: A Critical Survey of "Bias" in NLP](https://mlsanity.com/paper/2005.14050/) (2020) - [Measurement and Fairness](https://mlsanity.com/paper/1912.05511/) (2019) - [Gender Bias in Neural Natural Language Processing](https://mlsanity.com/paper/1807.11714/) (2018) - [Fairness of Exposure in Rankings](https://mlsanity.com/paper/1802.07281/) (2018) - [Participatory Research for Low-resourced Machine Translation: A Case Study in African Languages](https://mlsanity.com/paper/2010.02353/) (2020) - [No Classification without Representation: Assessing Geodiversity Issues in Open Data Sets for the Developing World](https://mlsanity.com/paper/1711.08536/) (2017) - [Fairness in Machine Learning: Lessons from Political Philosophy](https://mlsanity.com/paper/1712.03586/) (2017) - [Power to the People? Opportunities and Challenges for Participatory AI](https://mlsanity.com/paper/2209.07572/) (2022) - [Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction](https://mlsanity.com/paper/2210.05791/) (2022) - [Participation is not a Design Fix for Machine Learning](https://mlsanity.com/paper/2007.02423/) (2020) - [It's All in the Name: Mitigating Gender Bias with Name-Based Counterfactual Data Substitution](https://mlsanity.com/paper/1909.00871/) (2019) - [How well can Text-to-Image Generative Models understand Ethical Natural Language Interventions?](https://mlsanity.com/paper/2210.15230/) (2022) - [Survey of Bias In Text-to-Image Generation: Definition, Evaluation, and Mitigation](https://mlsanity.com/paper/2404.01030/) (2024) - [A Moral Framework for Understanding of Fair ML through Economic Models of Equality of Opportunity](https://mlsanity.com/paper/1809.03400/) (2018) - [Fair and Diverse DPP-based Data Summarization](https://mlsanity.com/paper/1802.04023/) (2018) - [Explainability Pitfalls: Beyond Dark Patterns in Explainable AI](https://mlsanity.com/paper/2109.12480/) (2021) - [Measuring Representational Harms in Image Captioning](https://mlsanity.com/paper/2206.07173/) (2022) - [Fair Text-to-Image Diffusion via Fair Mapping](https://mlsanity.com/paper/2311.17695/) (2023) - [Stereotypes and Smut: The (Mis)representation of Non-cisgender Identities by Text-to-Image Models](https://mlsanity.com/paper/2305.17072/) (2023) - [AHA!: Facilitating AI Impact Assessment by Generating Examples of Harms](https://mlsanity.com/paper/2306.03280/) (2023) - [Do Generative AI Models Output Harm while Representing Non-Western Cultures: Evidence from A Community-Centered Approach](https://mlsanity.com/paper/2407.14779/) (2024) - [Taxonomizing and Measuring Representational Harms: A Look at Image Tagging](https://mlsanity.com/paper/2305.01776/) (2023) - [Beyond Behaviorist Representational Harms: A Plan for Measurement and Mitigation](https://mlsanity.com/paper/2402.01705/) (2024) - [From Representational Harms to Quality-of-Service Harms: A Case Study on Llama 2 Safety Safeguards](https://mlsanity.com/paper/2403.13213/) (2024) - [Safety and Fairness for Content Moderation in Generative Models](https://mlsanity.com/paper/2306.06135/) (2023) - [Algorithmic Fairness and Structural Injustice: Insights from Feminist Political Philosophy](https://mlsanity.com/paper/2206.00945/) (2022) - [Distributive Justice and Fairness Metrics in Automated Decision-making: How Much Overlap Is There?](https://mlsanity.com/paper/2105.01441/) (2021) - ["I don't see myself represented here at all": User Experiences of Stable Diffusion Outputs Containing Representational Harms across Gender Identities and Nationalities](https://mlsanity.com/paper/2408.01594/) (2024) - [More of the Same: Persistent Representational Harms Under Increased Representation](https://mlsanity.com/paper/2503.00333/) (2025) ## Built on by (most cited first, 1 of 1) - [What is Safety? Corporate Discourse, Power, and the Politics of Generative AI Safety](https://mlsanity.com/paper/2602.06981/) (2026) ## Abstract (arXiv) Fairness in machine learning (ML) has become a rapidly growing area of research. But why, in the first place, is unfairness in ML wrong? And why should we care about improving fairness? Most fair-ML research implicitly appeals to distributive equality: the idea that desirable benefits and goods, such as opportunities (e.g., Barocas et al., 2023), should be equally distributed across society. Unfair ML models, then, are seen as wrong because they unequally distribute such benefits. This paper argues that this exclusive focus on distributive equality offers an incomplete and potentially misleading ethical foundation. Grounding ML fairness in egalitarianism--the view that equality is a fundamental moral and social ideal--requires challenging structural inequality: systematic, institutional, and durable arrangements that privilege some groups while disadvantaging others. Structural inequality manifests through ML systems in two primary forms: allocative harms (e.g., economic loss) and representational harms (e.g., stereotypes, erasure). While distributive equality helps address allocative harms, it fails to explain why representational harms are wrong--why it is wrong for ML systems to reinforce social hierarchies that stratify people into superior and inferior groups--and why ML systems should aim to foster a society where people relate as equals (i.e., relational equality). To address these limitations, the paper proposes a multifaceted egalitarian framework for ML fairness that integrates both distributive and relational equality. Drawing on critical social and political philosophy, this framework offers a more comprehensive ethical foundation for tackling the full spectrum of harms perpetuated by ML systems. The paper also outlines practical pathways for implementing the framework across the entire ML pipeline.