An algorithmic framework for positive action

Thomas, Oliver, Zilka, Miri, Weller, Adrian and Quadrianto, Novi (2021) An algorithmic framework for positive action. ACM conference on Equity and Access in Algorithms, Mechanisms, and Optimization, Virtual, October 5-9, 2021. Published in: Proceedings of Equity and Access in Algorithms, Mechanisms, and Optimization. a18 1-13. Association for Computing Machinery ISBN 9781450385534

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Abstract

Positive action is defined within anti-discrimination legislation as voluntary, legal action taken to address an imbalance of opportunity affecting individuals belonging to under-represented groups. Within this theme, we propose a novel algorithmic fairness framework to advance equal representation while respecting anti-discrimination legislation and equal-treatment rights. We use a counterfactual fairness approach to assign one of three outcomes to each candidate: accept; reject; or flagged as a positive action candidate.

Item Type: Conference Proceedings
Keywords: Algorithmic Fairness, Auditing, Causal Inference
Schools and Departments: School of Engineering and Informatics > Informatics
SWORD Depositor: Mx Elements Account
Depositing User: Mx Elements Account
Date Deposited: 13 Sep 2021 08:19
Last Modified: 01 Mar 2022 13:19
URI: http://sro.sussex.ac.uk/id/eprint/101653

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BayesianGDPR - Bayesian Models and Algorithms for Fairness and TransparencyG2903EUROPEAN UNIONUnset