AI Ethics: A Bibliometric Analysis, Critical Issues, and Key Gaps
It addresses the need for structured understanding of AI ethics research trends and gaps for scholars and policymakers, but is incremental as it synthesizes existing literature without new empirical findings.
This study conducted a bibliometric analysis of AI ethics literature over two decades, identifying a tripartite progression and seven key issues, while highlighting research gaps in large ethics models and AI identification.
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.