Biases and Ethical Considerations in AI-Driven Knowledge Management

Abstract Artificial Intelligence (AI) systems have become an integrated part of almost every aspect of contemporary organizations’ work environments. At a time when AI changes the way organizations acquire, create, and share knowledge, AI-driven Knowledge Management (KM) requires the re-evaluation of ethical scrutiny of how information is created, accessed, and preserved. Through this chapter, the authors utilize the KM Mesosystem Model to analyze, discuss, and reflect on the potential influences of AI on KM systems and processes. By detailed analysis of the model’s layers and each of the four categories compounding it, the authors describe how different types of biases can appear in each of them and discuss their possible impacts. Besides, the authors provide actionable recommendations for mitigating the representative types of human, systemic, and algorithmic biases in AI-based KM systems. Finally, by synthesizing insights across disciplines, the authors explain how the mitigation of biases and ethical issues could be used for AI-driven management of knowledge-related systems and processes.