Haldar, Aparajita, Cunningham, Teddy and Ferhatosmanoglu, Hakan (2022) RAGUEL : Recourse-Aware Group Unfairness Elimination. In: 31st ACM International Conference on Information and Knowledge Management (CIKM ’22), Atlanta, GA, USA, 17–21 Oct 2022. Published in: Proceedings of the 31st ACM International Conference on Information and Knowledge Management (CIKM ’22) pp. 666-675. ISBN 9781450392365. doi:10.1145/3511808.3557424

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Abstract

While machine learning and ranking-based systems are in widespread use for sensitive decision-making processes (e.g., determining job candidates, assigning credit scores), they are rife with concerns over unintended biases in their outcomes, which makes algorithmic fairness (e.g., demographic parity, equal opportunity) an objective of interest. 'Algorithmic recourse' offers feasible recovery actions to change unwanted outcomes through the modification of attributes. We introduce the notion of ranked group-level recourse fairness, and develop a 'recourse-aware ranking' solution that satisfies ranked recourse fairness constraints while minimizing the cost of suggested modifications. Our solution suggests interventions that can reorder the ranked list of database records and mitigate group-level unfairness; specifically, disproportionate representation of sub-groups and recourse cost imbalance. This re-ranking identifies the minimum modifications to data points, with these attribute modifications weighted according to their ease of recourse. We then present an efficient block-based extension that enables re-ranking at any granularity (e.g., multiple brackets of bank loan interest rates, multiple pages of search engine results). Evaluation on real datasets shows that, while existing methods may even exacerbate recourse unfairness, our solution – RAGUEL – significantly improves recourse-aware fairness. RAGUEL outperforms alternatives at improving recourse fairness, through a combined process of counterfactual generation and re-ranking, whilst remaining efficient for large-scale datasets.

Item Type:	Conference Item (Paper)
Divisions:	Faculty of Science, Engineering and Medicine > Science > Computer Science
Journal or Publication Title:	Proceedings of the 31st ACM International Conference on Information and Knowledge Management (CIKM ’22)
Publisher:	ACM
ISBN:	9781450392365
Official Date:	17 October 2022
Dates:	Date Event 17 October 2022 Published 2 August 2022 Accepted
Page Range:	pp. 666-675
DOI:	10.1145/3511808.3557424
Status:	Peer Reviewed
Publication Status:	Published
Reuse Statement (publisher, data, author rights):	© ACM, 2022. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in -CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management, October 2022 http://doi.acm.org/10.1145/3511808.3557424
Access rights to Published version:	Restricted or Subscription Access
Date of first compliant deposit:	25 August 2022
RIOXX Funder/Project Grant:	Project/Grant ID RIOXX Funder Name Funder ID EP/L016400/1 [EPSRC] Engineering and Physical Sciences Research Council http://dx.doi.org/10.13039/501100000266
Conference Paper Type:	Paper
Title of Event:	31st ACM International Conference on Information and Knowledge Management (CIKM ’22)
Type of Event:	Conference
Location of Event:	Atlanta, GA, USA
Date(s) of Event:	17–21 Oct 2022
Related URLs:	Organisation

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