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AI Ethics and Transparency in Operations Management: How Governance Mechanisms Can Reduce Data Bias and Privacy Risks
- 1 Hong Kong Polytechnic University
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Abstract
The use of artificial intelligence (AI) in operations management holds the key to efficiency, precision and agility in business decision-making, yet it also involves ethical challenges such as fairness, accountability, transparency and privacy that can undermine trust in AI. This paper examines the ethical considerations of AI use in operations, paying particular attention to data bias, privacy risks and governance. Drawing on major governance frameworks such as the OECD AI Principles and the EU’s Ethics Guidelines for Trustworthy AI, this paper proposes a hybrid governance model to address the unique challenges of operational contexts. A case study in the financial sector is used to further explain how privacy-preserving techniques can safeguard the sensitive customer data needed for AI-driven customer service. Extensive experimentation conducted in that case has shown that privacy-preserving methods such as differential privacy and federated learning can reduce the incidence of unauthorised data-access events by as much as 30 per cent and can improve customer satisfaction by more than 20 per cent. This paper contributes to the dynamic discourse on ethical AI by offering practical recommendations to organisations on how to conduct AI operations in a way that is responsible and compliant.
Keywords
AI ethics, operations management, data bias, privacy risks, governance frameworks
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Cite this article
Li,Z. (2024). AI Ethics and Transparency in Operations Management: How Governance Mechanisms Can Reduce Data Bias and Privacy Risks. Journal of Applied Economics and Policy Studies,13,89-93.
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Journal：Journal of Applied Economics and Policy Studies
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