No Cover Image

Journal article 1034 views 674 downloads

Addressing Algorithmic Bias in AI-Driven Customer Management

Shahriar Akter, Yogesh Dwivedi Orcid Logo, 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 Orcid Logo

  • 55933.pdf

    PDF | Version of Record

    This article published as an Open Access article distributed under the terms of the Creative Commons Attribution License

    Download (583.34KB)

Check full text

DOI (Published version): 10.4018/jgim.20211101.oa3

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...

Full description

Published in: Journal of Global Information Management
ISSN: 1062-7375 1533-7995
Published: IGI Global 2021
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa55933
Tags: Add Tag
No Tags, Be the first to tag this record!
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, 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.
Keywords: AI Ethics, Algorithm Bias, Artificial Intelligence, Machine Learning, Responsible AI
College: Faculty of Humanities and Social Sciences
Issue: 6
Start Page: 1
End Page: 27

Similar Items