AI Ethics: A Bibliometric Analysis, Critical Issues, and Key Gaps
<|reference_start|>AI Ethics: A Bibliometric Analysis, Critical Issues, and Key Gaps: 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.<|reference_end|>
@article{gao2024ai,
    title={AI Ethics: A Bibliometric Analysis, Critical Issues, and Key Gaps},
    author={Di Kevin Gao (1,2), Andrew Haverly (1), Sudip Mittal (1), Jiming Wu
  (2), Jingdao Chen (1) ((1) Mississippi State University, (2) California State
  University - East Bay)},
    journal={International Journal of Business Analytics (IJBAN), 2024, 11(1),
  1-19},
    year={2024},
    doi={10.4018/IJBAN.338367},
    archivePrefix={arXiv},
    eprint={2403.14681},
    primaryClass={cs.CY cs.AI}
}