Addressing Algorithmic Bias in AI-Driven Customer Management

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

Research output: Contribution to journalArticlepeer-review


Research on AI has gained momentum in recent years. Many scholars and practitioners have been increasingly highlighting 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.

Original languageEnglish (US)
Article number3
JournalJournal of Global Information Management
Issue number6
StatePublished - 2021
Externally publishedYes


  • AI Ethics
  • Algorithm Bias
  • Artificial Intelligence
  • Machine Learning
  • Responsible AI

ASJC Scopus subject areas

  • Business and International Management
  • Computer Science Applications
  • Strategy and Management
  • Management Science and Operations Research
  • Information Systems and Management


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