Machine Bias and Fairness

Nusirat Ojuolape Gold () and Husain Coovadia ()
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Nusirat Ojuolape Gold: University of Johannesburg, Department of Commercial Accounting, College of Business and Economics
Husain Coovadia: University of Johannesburg, Department of Commercial Accounting, College of Business and Economics

Chapter 2 in AI and Accountability, 2026, pp 19-40 from Springer

Abstract: Abstract Organizations around the globe have been utilizing artificial intelligence (AI) for different business processes. While these tools offer numerous benefits, they have several associated risks that are debatable. Resulting in stakeholders getting agitated, particularly on concerns related to machine bias, and the lack of fairness continues to negatively impact the different aspects of business processes, consequently raising ethical concerns. Exploring the various definitions of machine bias, this chapter provides a detailed explanation of the fairness concept from varying perspectives to provide a comprehensive insight that can foster an understanding of the intersections between machine bias and fairness. To achieve this objective, the chapter employed an extensive review of contemporary research and established that bias is an issue prevalent across domains, including business and finance. To overcome bias in the AI system, the chapter explored a series of techniques covering technical, governance, and operational approaches that can be employed. By so doing, the chapter contributes to ongoing debates on how AI design and deployments can uphold ethical consideration, ensuring fairness in the system, promoting the inclusiveness of all users, without undermining stakeholders’ trust.

Keywords: Artificial intelligence; Machine bias; Fairness; Mitigation strategy; Integrative pathways; Societal expectation (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:csrchp:978-3-032-20091-4_2

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DOI: 10.1007/978-3-032-20091-4_2

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