AI in knowledge sharing, which ethical challenges are raised in decision-making processes for organisations?
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Purpose: 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 (DM) processes within organisations. Design/methodology/approach: 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 assess their impact on DM processes. Findings: The findings reveal that challenges related to privacy and data protection, bias and fairness and transparency and explainability are particularly significant in DM. Moreover, challenges related to accountability and responsibility and the impact of AI on employment also show relatively high coefficients, highlighting their importance in the DM 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 DM process. Originality/value: This research contributes to the ongoing discourse on the ethical challenges of AI in knowledge management (KM) and DM 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 whilst mitigating their associated risks.
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Consumers lose agency over how they are represented in data.
“Furthermore, consumers lose agency over how they are represented in data (Rawas, 2024;Rezaei et al., 2024;Samara et al., 2024).”
From Surveillance Capitalism, Consumer Subjectivity and Marketing in the Age of Artificial Intelligence (2025) · cites this paper as Rezaei et al., 2024
Section 2.3 discusses informed consent and individuals’ autonomy in the use of their data, while the Discussion addresses privacy and data misuse. Neither says that consumers lose control over how they are represented in data. Searches for consumer agency, control, and data representation found no passage making that point. (LLM, conf=0.72)
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AI-Driven Logistics: Reshaping Public Sector Management and Workforce Dynamics
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- Authors
- Rezaei, M., Pironti, M., & Quaglia, R.
- Affiliations
- Università Cattolica del Sacro Cuore (IT)
- University of Turin (IT)
- ESCP Business School (GB)
- Venue
- Management Decision
- Publisher
- Emerald
- Published
- 2024-04-25
- Volume
- 63
- Issue
- 10
- Pages
- 3369-3388
- ISSN
- 0025-1747 (ISSN-L)
- 1758-6070
- OpenAlex
- W4395071526
- Cited by
- 123
- References
- 77
- Citations by year
- 2026: 69 · 2025: 50 · 2024: 4
- FWCI
- 51.21
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- other-oa
- Locations
- Management Decision (publishedVersion)
- Institutional Research Information System University of Turin (University of Turin) (submittedVersion, other-oa)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Decision Sciences › Management Science and Operations Research › Knowledge Management and Technology
- Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
- Keywords
- AI ethics, knowledge sharing, knowledge management, organizational decision-making, privacy and data protection, algorithmic bias, transparency and explainability, accountability and responsibility, AI and employment, intellectual property, algorithmic manipulation, AI regulation
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