Escaping the Impossibility of Fairness: From Formal to Substantive Algorithmic Fairness

Abstract

Efforts to promote equitable public policy with algorithms appear to be fundamentally constrained by the "impossibility of fairness" (an incompatibility between mathematical definitions of fairness). This technical limitation raises a central question about algorithmic fairness: How can computer scientists and policymakers support equitable policy reforms with algorithms? In this article, I argue that promoting justice with algorithms requires reforming the methodology of algorithmic fairness. First, I diagnose the problems of the current methodology for algorithmic fairness, which I call "formal algorithmic fairness." Because formal algorithmic fairness restricts analysis to isolated decision-making procedures, it leads to the impossibility of fairness and to models that exacerbate oppression despite appearing "fair." Second, I draw on theories of substantive equality from law and philosophy to propose an alternative methodology, which I call "substantive algorithmic fairness." Because substantive algorithmic fairness takes a more expansive scope of analysis, it enables an escape from the impossibility of fairness and provides a rigorous guide for alleviating injustice with algorithms. In sum, substantive algorithmic fairness presents a new direction for algorithmic fairness: away from formal mathematical models of "fair" decision-making and toward substantive evaluations of whether and how algorithms can promote justice in practice.

References in corpus (6)

• Fairness in Machine Learning: Lessons from Political Philosophy
• Measurement and Fairness
• Problem Formulation and Fairness
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• Interventions over Predictions: Reframing the Ethical Debate for Actuarial Risk Assessment
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Cited by in corpus (9)

• The Flaws of Policies Requiring Human Oversight of Government Algorithms
• Data Science as Political Action: Grounding Data Science in a Politics of Justice
• The Contestation of Tech Ethics: A Sociotechnical Approach to Technology Ethics in Practice
• Measuring Disparate Outcomes of Content Recommendation Algorithms with Distributional Inequality Metrics
• Algorithmic Pluralism: A Structural Approach To Equal Opportunity
• From the Fair Distribution of Predictions to the Fair Distribution of Social Goods: Evaluating the Impact of Fair Machine Learning on Long-Term Unemployment
• On the Richness of Calibration
• Pessimistic Evaluation
• Toward Substantive Intersectional Algorithmic Fairness: Desiderata for a Feminist Approach