Algorithmic Fairness From the Perspective of Legal Anti-discrimination Principles

24 Pages Posted: 24 May 2022

See all articles by Vijay Keswani

Vijay Keswani

Yale University; Duke University

L. Elisa Celis

Yale University

Date Written: May 01, 2024

Abstract

Real-world applications of Machine Learning (ML) algorithms often propagate negative stereotypes and social biases against marginalized groups. In response, the field of fair machine learning has proposed technical solutions for a variety of settings that aim to correct the biases in algorithmic predictions. These solutions remove the dependence of the final prediction on the protected attributes (like gender or race) and/or ensure that prediction performance is similar across demographic groups. Yet, recent studies assessing the impact of these solutions in practice demonstrate their ineffectiveness in tackling real-world inequalities. Given this lack of real-world success, it is essential to take a step back and question the design motivations of algorithmic fairness interventions. We use popular legal anti-discriminatory principles, specifically anti-classification and antisubordination principles, to study the motivations of fairness interventions and their applications. The anti-classification principle suggests addressing discrimination by ensuring that decision processes and outcomes are independent of the protected attributes of individuals. The anti-subordination principle, on the other hand, argues that decision-making policies can provide equal protection to all only by actively tackling societal hierarchies that enable structural discrimination, even if that requires using protected attributes to address historical inequalities. Through a survey of the fairness mechanisms and applications, we assess different components of fair ML approaches from the perspective of these principles. We argue that the observed shortcomings of fair ML algorithms are similar to the failures of anti-classification policies and that these shortcomings constitute violations of the anti-subordination principle. Correspondingly, we propose guidelines for algorithmic fairness interventions to adhere to the anti-subordination principle. In doing so, we hope to bridge critical concepts between legal frameworks for non-discrimination and fairness in machine learning.

Keywords: algorithmic fairness, anti-discrimination

Suggested Citation

Keswani, Vijay and Celis, L. Elisa, Algorithmic Fairness From the Perspective of Legal Anti-discrimination Principles (May 01, 2024). Available at SSRN: https://ssrn.com/abstract=4116835 or http://dx.doi.org/10.2139/ssrn.4116835

Vijay Keswani (Contact Author)

Yale University ( email )

493 College St
New Haven, CT CT 06520
United States

Duke University ( email )

100 Fuqua Drive
Durham, NC 27708-0204
United States

L. Elisa Celis

Yale University ( email )

493 College St
New Haven, CT CT 06520
United States
203.432.0666 (Phone)

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