What Is the Point of Equality in Machine Learning Fairness? Beyond Equality of Opportunity

• URL: http://arxiv.org/abs/2506.16782v1
• Date: Fri, 20 Jun 2025 06:57:53 GMT
• Title: What Is the Point of Equality in Machine Learning Fairness? Beyond Equality of Opportunity
• Authors: Youjin Kong,
• Abstract summary: Machine learning (ML) has become a rapidly growing area of research.<n>But why, in the first place, is unfairness in ML morally wrong?<n>This paper argues that this exclusive focus on distributive equality offers an incomplete and potentially misleading ethical foundation.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Fairness in machine learning (ML) has become a rapidly growing area of research. But why, in the first place, is unfairness in ML morally wrong? And why should we care about improving fairness? Most fair-ML research implicitly appeals to distributive equality: the idea that desirable goods and benefits, 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 ML pipeline.

Related papers

• The AI Fairness Myth: A Position Paper on Context-Aware Bias [0.0]
  We argue that fairness sometimes requires deliberate, context-aware preferential treatment of historically marginalized groups.<n>Rather than viewing bias solely as a flaw to eliminate, we propose a framework that embraces corrective, intentional biases.
  arXiv  Detail & Related papers  (2025-05-02T02:47:32Z)
• The Impossibility of Fair LLMs [17.812295963158714]
  We analyze a variety of technical fairness frameworks and find inherent challenges in each that make the development of a fair language model intractable.<n>We show that each framework either does not extend to the general-purpose AI context or is infeasible in practice.<n>These inherent challenges would persist for general-purpose AI, including LLMs, even if empirical challenges, such as limited participatory input and limited measurement methods, were overcome.
  arXiv  Detail & Related papers  (2024-05-28T04:36:15Z)
• Fairness-Accuracy Trade-Offs: A Causal Perspective [58.06306331390586]
  We analyze the tension between fairness and accuracy from a causal lens for the first time.<n>We show that enforcing a causal constraint often reduces the disparity between demographic groups.<n>We introduce a new neural approach for causally-constrained fair learning.
  arXiv  Detail & Related papers  (2024-05-24T11:19:52Z)
• Causal Fairness for Outcome Control [68.12191782657437]
  We study a specific decision-making task called outcome control in which an automated system aims to optimize an outcome variable $Y$ while being fair and equitable. In this paper, we first analyze through causal lenses the notion of benefit, which captures how much a specific individual would benefit from a positive decision. We then note that the benefit itself may be influenced by the protected attribute, and propose causal tools which can be used to analyze this.
  arXiv  Detail & Related papers  (2023-06-08T09:31:18Z)
• Factoring the Matrix of Domination: A Critical Review and Reimagination of Intersectionality in AI Fairness [55.037030060643126]
  Intersectionality is a critical framework that allows us to examine how social inequalities persist. We argue that adopting intersectionality as an analytical framework is pivotal to effectively operationalizing fairness.
  arXiv  Detail & Related papers  (2023-03-16T21:02:09Z)
• The Unfairness of Fair Machine Learning: Levelling down and strict egalitarianism by default [10.281644134255576]
  This paper examines the causes and prevalence of levelling down across fairML. We propose a first step towards substantive equality in fairML by design through enforcement of minimum acceptable harm thresholds.
  arXiv  Detail & Related papers  (2023-02-05T15:22:43Z)
• It's Not Fairness, and It's Not Fair: The Failure of Distributional Equality and the Promise of Relational Equality in Complete-Information Hiring Games [2.8935588665357077]
  existing discrimination and injustice is often the result of unequal social relations, rather than an unequal distribution of resources. We show how optimizing for existing computational and economic definitions of fairness and equality fail to prevent unequal social relations.
  arXiv  Detail & Related papers  (2022-09-12T20:35:42Z)
• How Robust is Your Fairness? Evaluating and Sustaining Fairness under Unseen Distribution Shifts [107.72786199113183]
  We propose a novel fairness learning method termed CUrvature MAtching (CUMA) CUMA achieves robust fairness generalizable to unseen domains with unknown distributional shifts. We evaluate our method on three popular fairness datasets.
  arXiv  Detail & Related papers  (2022-07-04T02:37:50Z)
• Fair Machine Learning in Healthcare: A Review [90.22219142430146]
  We analyze the intersection of fairness in machine learning and healthcare disparities. We provide a critical review of the associated fairness metrics from a machine learning standpoint. We propose several new research directions that hold promise for developing ethical and equitable ML applications in healthcare.
  arXiv  Detail & Related papers  (2022-06-29T04:32:10Z)
• What Is Fairness? On the Role of Protected Attributes and Fictitious Worlds [8.223468651994352]
  A growing body of literature in fairness-aware machine learning (fairML) aims to mitigate machine learning (ML)-related unfairness in automated decision-making (ADM)<n>However, the underlying concept of fairness is rarely discussed, leaving a significant gap between centuries of philosophical discussion and the recent adoption of the concept in the ML community.<n>We try to bridge this gap by formalizing a consistent concept of fairness and by translating the philosophical considerations into a formal framework for the training and evaluation of ML models in ADM systems.
  arXiv  Detail & Related papers  (2022-05-19T15:37:26Z)
• The zoo of Fairness metrics in Machine Learning [62.997667081978825]
  In recent years, the problem of addressing fairness in Machine Learning (ML) and automatic decision-making has attracted a lot of attention. A plethora of different definitions of fairness in ML have been proposed, that consider different notions of what is a "fair decision" in situations impacting individuals in the population. In this work, we try to make some order out of this zoo of definitions.
  arXiv  Detail & Related papers  (2021-06-01T13:19:30Z)
• Distributive Justice and Fairness Metrics in Automated Decision-making: How Much Overlap Is There? [0.0]
  We show that metrics implementing equality of opportunity only apply when resource allocations are based on deservingness, but fail when allocations should reflect concerns about egalitarianism, sufficiency, and priority. We argue that by cleanly distinguishing between prediction tasks and decision tasks, research on fair machine learning could take better advantage of the rich literature on distributive justice.
  arXiv  Detail & Related papers  (2021-05-04T12:09:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.