AI in knowledge sharing, which ethical challenges are raised in decision-making processes for organisations?

This study aims to identify and assess the key ethical challenges associated with integrating Artificial Intelligence (AI) in knowledge-sharing (KS) practices and their implications for decision-making processes within organisations. The research explores the ethical dimensions of AI-driven KS, such as privacy and data protection, bias and fairness, and transparency and explainability. The study employs a mixed-methods approach, beginning with a comprehensive literature review to extract background information on AI and KS and to identify potential ethical challenges. Subsequently, a confirmatory factor analysis (CFA) is conducted using data collected from individuals employed in business settings to validate the challenges identified in the literature and to assess their impact on decision-making processes. The findings reveal that challenges related to privacy and data protection, bias and fairness, and transparency and explainability are particularly significant in AI-driven decision-making. Moreover, challenges related to accountability and responsibility and the impact of AI on employment also show relatively high coefficients, highlighting their importance in the decision-making process. In contrast, challenges such as intellectual property and ownership, algorithmic manipulation, and global governance and regulation are found to be less central to the decision-making process. This research contributes to the ongoing discourse on the ethical challenges of AI in knowledge management and decision-making within organisations. By providing insights and recommendations for researchers, managers, and policymakers, the study emphasises the need for a holistic and collaborative approach to harness the benefits of AI technologies while mitigating their associated risks.