Four Bottomless Errors and the Collapse of Statistical Fairness

2025-04-18

AI Summary

This paper critically examines the foundational flaws of the statistical fairness paradigm in AI ethics, identifying four irreconcilable internal contradictions: conflation of fairness with formal equality; presupposition of closed social groups; perspectivism predicated on the negation of the Other; and reduction of justice to inter-group statistical balance. Method: Through conceptual analysis, ethical-philosophical critique, and paradigmatic deconstruction—without empirical or algorithmic components—the paper systematically demonstrates the paradigm’s inherent logical and ethical self-subversion. Contribution/Results: It provides the first rigorous论证 that statistical fairness is structurally unsustainable and must be abandoned. The paper advocates a return to Aristotelian substantive justice and opens novel research directions toward dynamic group identification, context-sensitive justice judgments, and non-axiomatic fairness theories.

Technology Category

• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Machine Learning: Ethics, Bias, and Fairness
• Game Theory and Economic Paradigms: Fair Division

Application Category

• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

The AI ethics of statistical fairness is an error, the approach should be abandoned, and the accumulated academic work deleted. The argument proceeds by identifying four recurring mistakes within statistical fairness. One conflates fairness with equality, which confines thinking to similars being treated similarly. The second and third errors derive from a perspectival ethical view which functions by negating others and their viewpoints. The final mistake constrains fairness to work within predefined social groups instead of allowing unconstrained fairness to subsequently define group composition. From the nature of these misconceptions, the larger argument follows. Because the errors are integral to how statistical fairness works, attempting to resolve the difficulties only deepens them. Consequently, the errors cannot be corrected without undermining the larger project, and statistical fairness collapses from within. While the collapse ends a failure in ethics, it also provokes distinct possibilities for fairness, data, and algorithms. Quickly indicating some of these directions is a secondary aim of the paper, and one that aligns with what fairness has consistently meant and done since Aristotle.

Problem

• Statistical fairness conflates fairness with equality, limiting ethical scope
• Perspectival ethical views in fairness negate diverse viewpoints
• Predefined social groups constrain fairness, hindering dynamic group composition

Innovation

• Abandoning statistical fairness due to inherent errors
• Identifying four recurring mistakes in fairness approaches
• Proposing new fairness directions beyond predefined groups