AI Ethics: A Bibliometric Analysis, Critical Issues, and Key Gaps
Overview
Authors: Kevin Gao, Andrew Haverly, Sudip Mittal, Jiming Wu, Jingdao Chen
Publication Date: 12 March 2024
Link: https://arxiv.org/pdf/2403.14681
Keywords: AI ethics, bibliomentric analysis, human-centric AI, Collingridge Dilemma, AI transparency, privacy protections, algocracy, superintelligence
Type: Peer-Reviewed Journals/White Papers
Summary
AI ethics has rapidly become a crucial area of academic inquiry. This study provides a thorough bibliometric analysis of AI ethics literature over the past two decades, identifying a three-phase progression: an initial incubation period, a phase centered on embedding human-like attributes into AI, and a final phase prioritizing human-centric AI development. The study highlights seven major ethical challenges, including the Collingridge dilemma, debates over AI's moral status, issues of transparency and explainability, privacy concerns, considerations of justice and fairness, the risks of algocracy and human dependence, and concerns surrounding superintelligence. Additionally, it identifies two key research gaps in AI ethics—the need for large ethics models (LEM) and AI identification mechanisms—while calling for further scholarly exploration in these areas.
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