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Disaggregated interventions to reduce inequality

Bynum, Lucius, Loftus, Joshua and Stoyanovich, Julia (2021) Disaggregated interventions to reduce inequality. In: Proceedings of 2021 ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization, EAAMO 2021. ACM International Conference Proceeding Series. Association for Computing Machinery, New York, NY. ISBN 9781450385534

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Identification Number: 10.1145/3465416.3483286

Abstract

A significant body of research in the data sciences considers unfair discrimination against social categories such as race or gender that could occur or be amplified as a result of algorithmic decisions. Simultaneously, real-world disparities continue to exist, even before algorithmic decisions are made. In this work, we draw on insights from the social sciences brought into the realm of causal modeling and constrained optimization, and develop a novel algorithmic framework for tackling pre-existing real-world disparities. The purpose of our framework, which we call the "impact remediation framework,"is to measure real-world disparities and discover the optimal intervention policies that could help improve equity or access to opportunity for those who are underserved with respect to an outcome of interest. We develop a disaggregated approach to tackling pre-existing disparities that relaxes the typical set of assumptions required for the use of social categories in structural causal models. Our approach flexibly incorporates counterfactuals and is compatible with various ontological assumptions about the nature of social categories. We demonstrate impact remediation with a hypothetical case study and compare our disaggregated approach to an existing state-of-the-art approach, comparing its structure and resulting policy recommendations. In contrast to most work on optimal policy learning, we explore disparity reduction itself as an objective, explicitly focusing the power of algorithms on reducing inequality.

Item Type: Book Section
Official URL: https://dl.acm.org/doi/proceedings/10.1145/3465416
Additional Information: © 2021 ACM.
Divisions: Statistics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
H Social Sciences > HM Sociology
Date Deposited: 06 Dec 2021 14:33
Last Modified: 20 Dec 2021 15:12
URI: http://eprints.lse.ac.uk/id/eprint/112795

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