Reference Hub

This research has been cited in:

Article
The Re-Conception of AI: Beyond Artificial, and Beyond IntelligenceIEEE Transactions on Technology and Society10.1109/TTS.2023.3234051
Article
Information systems security resilience as a dynamic capabilityJournal of Enterprise Information Management10.1108/JEIM-07-2022-0228
Article
Using machine learning to investigate consumers' emotions: the spillover effect of AI defeating people on consumers' attitudes toward AI companiesInternet Research10.1108/INTR-02-2022-0113
Article
Machine Learning, Convergence Digitalization, and the Concentration of Power: Enslavement by Design Using Techno-Biological BehaviorsIEEE Transactions on Technology and Society10.1109/TTS.2022.3179756
Article
Investigating the Determinants of New Technology Entrepreneurial Performance: an Empirical Study with PLS-SEM and MGAJournal of the Knowledge Economy10.1007/s13132-023-01359-w
Article
Advancing algorithmic bias management capabilities in AI-driven marketing analytics researchIndustrial Marketing Management10.1016/j.indmarman.2023.08.013
Article
AI in education risk assessment mechanism analysisApplied Economics10.1080/00036846.2024.2321835
Article
What prompts consumers to purchase online? A machine learning approachElectronic Commerce Research10.1007/s10660-022-09624-x
Article
Ethical framework for Artificial Intelligence and Digital technologiesInternational Journal of Information Management10.1016/j.ijinfomgt.2021.102433
Chapter
A Scoping Study of Ethics in Artificial Intelligence Research in Tourism and HospitalityInformation and Communication Technologies in Tourism 202310.1007/978-3-031-25752-0_26
Article
Mastering digital transformation in workforce managementProduction Planning & Control10.1080/09537287.2023.2270465
Article
AI Privacy Opinions between US and Chinese PeopleJournal of Computer Information Systems10.1080/08874417.2022.2079107
Article
A big-data-driven matching model based on deep reinforcement learning for cotton blendingInternational Journal of Production Research10.1080/00207543.2022.2153942
Article
Trust in Fintech: Risk, Governance, and Continuance IntentionJournal of Computer Information Systems10.1080/08874417.2022.2093295
Article
Biometrics and AI BiasIEEE Transactions on Technology and Society10.1109/TTS.2022.3156405
Article
A seven-layer model with checklists for standardising fairness assessment throughout the AI lifecycleAI and Ethics10.1007/s43681-023-00266-9
Chapter
AI Integration in E-Commerce WishlistsMarketing Innovation Strategies and Consumer Behavior10.4018/979-8-3693-4195-7.ch003
Article
Application of Machine Learning Algorithm in Managing Deviant Consumer Behaviors and Enhancing Public Service.Journal of Global Information Management10.4018/JGIM.292064
Article
Uncovering the Dark Side of Artificial Intelligence in Electronic MarketsJournal of Organizational and End User Computing10.4018/JOEUC.327278

Addressing Algorithmic Bias in AI-Driven Customer Management

Shahriar Akter, Yogesh K. Dwivedi, Kumar Biswas, Katina Michael, Ruwan J. Bandara, Shahriar Sajib

Source Title: Journal of Global Information Management (JGIM)29(6)

ISSN: 1062-7375|EISSN: 1533-7995|EISBN13: 9781799872627|DOI: 10.4018/JGIM.20211101.oa3

Cite Article Cite Article

MLA

Akter, Shahriar, et al. "Addressing Algorithmic Bias in AI-Driven Customer Management." JGIM vol.29, no.6 2021: pp.1-27. http://doi.org/10.4018/JGIM.20211101.oa3

APA

Akter, S., Dwivedi, Y. K., Biswas, K., Michael, K., Bandara, R. J., & Sajib, S. (2021). Addressing Algorithmic Bias in AI-Driven Customer Management. Journal of Global Information Management (JGIM), 29(6), 1-27. http://doi.org/10.4018/JGIM.20211101.oa3

Chicago

Akter, Shahriar, et al. "Addressing Algorithmic Bias in AI-Driven Customer Management," Journal of Global Information Management (JGIM) 29, no.6: 1-27. http://doi.org/10.4018/JGIM.20211101.oa3

Export Reference

Favorite Full-Issue Download

View Full Text HTML

View Full Text PDF

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.