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

Forthcoming, Philosophy & Technology

35 Pages Posted: 12 Jul 2021 Last revised: 21 Sep 2022

See all articles by Ben Green

Ben Green

University of Michigan at Ann Arbor - Society of Fellows; University of Michigan at Ann Arbor - Gerald R. Ford School of Public Policy; Harvard University - Berkman Klein Center for Internet & Society

Date Written: September 21, 2022

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 why the current methodology for algorithmic fairness— which I call “formal algorithmic fairness”—leads to the impossibility of fairness and to models that exacerbate oppression despite appearing “fair.” I demonstrate that the problems of algorithmic fairness result from the field’s methodology, which restricts analysis to isolated decision-making procedures. Second, I draw on theories of substantive equality from law and philosophy to propose an alternative methodology: “substantive algorithmic fairness.” Because substantive algorithmic fairness takes a more expansive scope to fairness, 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 how algorithms can (and cannot) promote justice.

Keywords: Algorithmic fairness, algorithmic bias, justice, substantive equality, discrimination, risk assessments

Suggested Citation

Green, Ben, Escaping the Impossibility of Fairness: From Formal to Substantive Algorithmic Fairness (September 21, 2022). Forthcoming, Philosophy & Technology, Available at SSRN: https://ssrn.com/abstract=3883649 or http://dx.doi.org/10.2139/ssrn.3883649

Ben Green (Contact Author)

University of Michigan at Ann Arbor - Society of Fellows ( email )

Ann Arbor, MI
United States

University of Michigan at Ann Arbor - Gerald R. Ford School of Public Policy ( email )

735 South State Street, Weill Hall
Ann Arbor, MI 48109
United States

Harvard University - Berkman Klein Center for Internet & Society ( email )

Harvard Law School
23 Everett, 2nd Floor
Cambridge, MA 02138
United States

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