Machine Learning and the Meaning of Equal Treatment

Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’21), May 19–21, 2021

11 Pages Posted: 24 Mar 2022

See all articles by Josh Simons

Josh Simons

Harvard University, Faculty of Arts and Sciences, Department of Government

Adrian Weller

University of Cambridge; The Alan Turing Institute

Sophia Adams Bhatti

University of Cambridge

Date Written: May 19, 2021

Abstract

Approaches to non-discrimination are generally informed by two principles: striving for equality of treatment, and advancing var- ious notions of equality of outcome. We consider when and why there are trade-offs in machine learning between respecting for- malistic interpretations of equal treatment and advancing equality of outcome. Exploring a hypothetical discrimination suit against Facebook, we argue that interpretations of equal treatment which require blindness to difference may constrain how machine learn- ing can be deployed to advance equality of outcome. When machine learning models predict outcomes that are unevenly distributed across racial groups, using those models to advance racial justice will often require deliberately taking race into account.

We then explore the normative stakes of this tension. We describe three pragmatic policy options underpinned by distinct interpreta- tions and applications of equal treatment. A status quo approach insists on blindness to difference, permitting the design of machine learning models that compound existing patterns of disadvantage. An industry-led approach would specify a narrow set of domains in which institutions were permitted to use protected characteris- tics to actively reduce inequalities of outcome. A government-led approach would impose positive duties that require institutions to consider how best to advance equality of outcomes and permit the use of protected characteristics to achieve that goal. We argue that while machine learning offers significant possibilities for advancing racial justice and outcome-based equality, harnessing those possibilities will require a shift in the normative commitments that underpin the interpretation and application of equal treatment in non-discrimination law and the governance of machine learning.

Keywords: artificial intelligence, machine learning, equality, civil rights, discrimination, justice, equal treatment, Aristotle

Suggested Citation

Simons, Josh and Weller, Adrian and Bhatti, Sophia Adams, Machine Learning and the Meaning of Equal Treatment (May 19, 2021). Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’21), May 19–21, 2021, Available at SSRN: https://ssrn.com/abstract=3678665

Josh Simons (Contact Author)

Harvard University, Faculty of Arts and Sciences, Department of Government ( email )

1737 Cambridge Street
Cambridge, MA 02138
United States

HOME PAGE: http://https://scholar.harvard.edu/joshua-simons/home

Adrian Weller

University of Cambridge ( email )

Trinity Ln
Cambridge, CB2 1TN
United Kingdom

The Alan Turing Institute ( email )

British Library, 96 Euston Road
London, NW12DB
United Kingdom

Sophia Adams Bhatti

University of Cambridge

Trinity Ln
Cambridge, CB2 1TN
United Kingdom

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