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AI Ethics: A Bibliometric Analysis, Critical Issues, and Key Gaps
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Author(s): Di Kevin Gao (California State University, East Bay, USA), Andrew Haverly (Mississippi State University, USA), Sudip Mittal (Mississippi State University, USA), Jiming Wu (California State University, East Bay, USA)and Jingdao Chen (Mississippi State University, USA)
Copyright: 2024
Volume: 11
Issue: 1
Pages: 19
Source title:
International Journal of Business Analytics (IJBAN)
Editor(s)-in-Chief: John Wang (Montclair State University, USA)
DOI: 10.4018/IJBAN.338367
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Abstract
Artificial intelligence (AI) ethics has emerged as a burgeoning yet pivotal area of scholarly research. This study conducts a comprehensive bibliometric analysis of the AI ethics literature over the past two decades. The analysis reveals a discernible tripartite progression, characterized by an incubation phase, followed by a subsequent phase focused on imbuing AI with human-like attributes, culminating in a third phase emphasizing the development of human-centric AI systems. After that, they present seven key AI ethics issues, encompassing the Collingridge dilemma, the AI status debate, challenges associated with AI transparency and explainability, privacy protection complications, considerations of justice and fairness, concerns about algocracy and human enfeeblement, and the issue of superintelligence. Finally, they identify two notable research gaps in AI ethics regarding the large ethics model (LEM) and AI identification and extend an invitation for further scholarly research.
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