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Enhancing Fairness, Justice and Accuracy of Hybrid Human-AI Decisions by Shifting Epistemological Stances

Peter Daish, Matt Roach Orcid Logo, Alan Dix

Communications in Computer and Information Science, Volume: 1, Pages: 323 - 331

Swansea University Authors: Peter Daish, Matt Roach Orcid Logo, Alan Dix

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DOI (Published version): 10.1007/978-3-031-74627-7_25

Abstract

From applications in automating credit to aiding judges in presiding over cases of recidivism, deep-learning powered AI systems are becoming embedded in high-stakes decision-making processes as either primary decision-makers or supportive assistants to humans in a hybrid decision-making context, wit...

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Published in: Communications in Computer and Information Science
ISBN: 9783031746260 9783031746277
ISSN: 1865-0929 1865-0937
Published: Cham Springer Nature Switzerland 2025
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URI: https://cronfa.swan.ac.uk/Record/cronfa68367
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last_indexed 2025-01-20T20:34:58Z
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spelling 2025-01-20T14:13:12.1663325 v2 68367 2024-11-28 Enhancing Fairness, Justice and Accuracy of Hybrid Human-AI Decisions by Shifting Epistemological Stances 526bb6b1afc3f8acae8bd6a962b107f8 Peter Daish Peter Daish true false 9722c301d5bbdc96e967cdc629290fec 0000-0002-1486-5537 Matt Roach Matt Roach true false e31e47c578b2a6a39949aa7f149f4cf9 Alan Dix Alan Dix true false 2024-11-28 MACS From applications in automating credit to aiding judges in presiding over cases of recidivism, deep-learning powered AI systems are becoming embedded in high-stakes decision-making processes as either primary decision-makers or supportive assistants to humans in a hybrid decision-making context, with the aim of improving the quality of decisions. However, the criteria currently used to assess a system’s ability to improve hybrid decisions is driven by a utilitarian desire to optimise accuracy through a phenomenon known as ‘complementary performance’. This desire puts the design of hybrid decision-making at odds with critical subjective concepts that affect the perception and acceptance of decisions, such as fairness. Fairness as a subjective notion often has a competitive relationship with accuracy and as such, driving complementary behaviour with a utilitarian belief risks driving unfairness in decisions. It is our position that shifting epistemological stances taken in the research and design of human-AI environments is necessary to incorporate the relationship between fairness and accuracy into the notion of ‘complementary behaviour’, in order to observe ‘enhanced’ hybrid human-AI decisions. Conference Paper/Proceeding/Abstract Communications in Computer and Information Science 1 323 331 Springer Nature Switzerland Cham 9783031746260 9783031746277 1865-0929 1865-0937 1 1 2025 2025-01-01 10.1007/978-3-031-74627-7_25 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University 2025-01-20T14:13:12.1663325 2024-11-28T11:38:30.2619395 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Peter Daish 1 Matt Roach 0000-0002-1486-5537 2 Alan Dix 3 68367__32988__3f75b94c8c4e44b290ef9876988fbc02.pdf 68367.pdf 2024-11-28T11:45:51.4039536 Output 418127 application/pdf Accepted Manuscript true Author accepted manuscript document released under the terms of a Creative Commons CC-BY licence using the Swansea University Research Publications Policy (rights retention). true eng https://creativecommons.org/licenses/by/4.0/deed.en
title Enhancing Fairness, Justice and Accuracy of Hybrid Human-AI Decisions by Shifting Epistemological Stances
spellingShingle Enhancing Fairness, Justice and Accuracy of Hybrid Human-AI Decisions by Shifting Epistemological Stances
Peter Daish
Matt Roach
Alan Dix
title_short Enhancing Fairness, Justice and Accuracy of Hybrid Human-AI Decisions by Shifting Epistemological Stances
title_full Enhancing Fairness, Justice and Accuracy of Hybrid Human-AI Decisions by Shifting Epistemological Stances
title_fullStr Enhancing Fairness, Justice and Accuracy of Hybrid Human-AI Decisions by Shifting Epistemological Stances
title_full_unstemmed Enhancing Fairness, Justice and Accuracy of Hybrid Human-AI Decisions by Shifting Epistemological Stances
title_sort Enhancing Fairness, Justice and Accuracy of Hybrid Human-AI Decisions by Shifting Epistemological Stances
author_id_str_mv 526bb6b1afc3f8acae8bd6a962b107f8
9722c301d5bbdc96e967cdc629290fec
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author_id_fullname_str_mv 526bb6b1afc3f8acae8bd6a962b107f8_***_Peter Daish
9722c301d5bbdc96e967cdc629290fec_***_Matt Roach
e31e47c578b2a6a39949aa7f149f4cf9_***_Alan Dix
author Peter Daish
Matt Roach
Alan Dix
author2 Peter Daish
Matt Roach
Alan Dix
format Conference Paper/Proceeding/Abstract
container_title Communications in Computer and Information Science
container_volume 1
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publishDate 2025
institution Swansea University
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description From applications in automating credit to aiding judges in presiding over cases of recidivism, deep-learning powered AI systems are becoming embedded in high-stakes decision-making processes as either primary decision-makers or supportive assistants to humans in a hybrid decision-making context, with the aim of improving the quality of decisions. However, the criteria currently used to assess a system’s ability to improve hybrid decisions is driven by a utilitarian desire to optimise accuracy through a phenomenon known as ‘complementary performance’. This desire puts the design of hybrid decision-making at odds with critical subjective concepts that affect the perception and acceptance of decisions, such as fairness. Fairness as a subjective notion often has a competitive relationship with accuracy and as such, driving complementary behaviour with a utilitarian belief risks driving unfairness in decisions. It is our position that shifting epistemological stances taken in the research and design of human-AI environments is necessary to incorporate the relationship between fairness and accuracy into the notion of ‘complementary behaviour’, in order to observe ‘enhanced’ hybrid human-AI decisions.
published_date 2025-01-01T08:39:08Z
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