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Addressing Algorithmic Bias in AI-Driven Customer Management / Shahriar Akter, Yogesh Dwivedi, Kumar Biswas, Katina Michael, Ruwan J. Bandara, Shahriar Sajib

Journal of Global Information Management, Volume: 29, Issue: 6, Pages: 1 - 27

Swansea University Author: Yogesh Dwivedi

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Abstract

Research on AI has gained momentum in recent years. Many scholars and practitioners increasingly highlight the dark sides of AI, particularly related to algorithm bias. This study elucidates situations in which AI-enabled analytics systems make biased decisions against customers based on gender, rac...

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Published in: Journal of Global Information Management
ISSN: 1062-7375 1533-7995
Published: IGI Global 2021
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URI: https://cronfa.swan.ac.uk/Record/cronfa55933
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first_indexed 2020-12-27T12:58:52Z
last_indexed 2021-09-18T03:18:20Z
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spelling 2021-09-17T12:35:25.4826692 v2 55933 2020-12-27 Addressing Algorithmic Bias in AI-Driven Customer Management d154596e71b99ad1285563c8fdd373d7 0000-0002-5547-9990 Yogesh Dwivedi Yogesh Dwivedi true false 2020-12-27 BBU Research on AI has gained momentum in recent years. Many scholars and practitioners increasingly highlight the dark sides of AI, particularly related to algorithm bias. This study elucidates situations in which AI-enabled analytics systems make biased decisions against customers based on gender, race, religion, age, nationality or socioeconomic status. Based on a systematic literature review, this research proposes two approaches (i.e., a priori and post-hoc) to overcome such biases in customer management. As part of a priori approach, the findings suggest scientific, application, stakeholder and assurance consistencies. With regard to the post-hoc approach, the findings recommend six steps: bias identification, review of extant findings, selection of the right variables, responsible and ethical model development, data analysis and action on insights. Overall, this study contributes to the ethical and responsible use of AI applications. Journal Article Journal of Global Information Management 29 6 1 27 IGI Global 1062-7375 1533-7995 AI Ethics, Algorithm Bias, Artificial Intelligence, Machine Learning, Responsible AI 1 11 2021 2021-11-01 10.4018/jgim.20211101.oa3 COLLEGE NANME Business COLLEGE CODE BBU Swansea University 2021-09-17T12:35:25.4826692 2020-12-27T12:55:45.0296402 School of Management Business Shahriar Akter 1 Yogesh Dwivedi 0000-0002-5547-9990 2 Kumar Biswas 3 Katina Michael 4 Ruwan J. Bandara 5 Shahriar Sajib 6 55933__20905__5c1ecad6c8f24b7caa50065ca5b49bd7.pdf 55933.pdf 2021-09-17T12:33:20.1743020 Output 597344 application/pdf Version of Record true This article published as an Open Access article distributed under the terms of the Creative Commons Attribution License true eng
title Addressing Algorithmic Bias in AI-Driven Customer Management
spellingShingle Addressing Algorithmic Bias in AI-Driven Customer Management
Yogesh, Dwivedi
title_short Addressing Algorithmic Bias in AI-Driven Customer Management
title_full Addressing Algorithmic Bias in AI-Driven Customer Management
title_fullStr Addressing Algorithmic Bias in AI-Driven Customer Management
title_full_unstemmed Addressing Algorithmic Bias in AI-Driven Customer Management
title_sort Addressing Algorithmic Bias in AI-Driven Customer Management
author_id_str_mv d154596e71b99ad1285563c8fdd373d7
author_id_fullname_str_mv d154596e71b99ad1285563c8fdd373d7_***_Yogesh, Dwivedi
author Yogesh, Dwivedi
author2 Shahriar Akter
Yogesh Dwivedi
Kumar Biswas
Katina Michael
Ruwan J. Bandara
Shahriar Sajib
format Journal article
container_title Journal of Global Information Management
container_volume 29
container_issue 6
container_start_page 1
publishDate 2021
institution Swansea University
issn 1062-7375
1533-7995
doi_str_mv 10.4018/jgim.20211101.oa3
publisher IGI Global
college_str School of Management
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hierarchy_top_title School of Management
hierarchy_parent_id schoolofmanagement
hierarchy_parent_title School of Management
department_str Business{{{_:::_}}}School of Management{{{_:::_}}}Business
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description Research on AI has gained momentum in recent years. Many scholars and practitioners increasingly highlight the dark sides of AI, particularly related to algorithm bias. This study elucidates situations in which AI-enabled analytics systems make biased decisions against customers based on gender, race, religion, age, nationality or socioeconomic status. Based on a systematic literature review, this research proposes two approaches (i.e., a priori and post-hoc) to overcome such biases in customer management. As part of a priori approach, the findings suggest scientific, application, stakeholder and assurance consistencies. With regard to the post-hoc approach, the findings recommend six steps: bias identification, review of extant findings, selection of the right variables, responsible and ethical model development, data analysis and action on insights. Overall, this study contributes to the ethical and responsible use of AI applications.
published_date 2021-11-01T04:18:20Z
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score 10.81997