R0058/2026-04-03/C001/SRC02
Qiu, Cheng & Huang (2025) — Independent bibliometric analysis of AI ethics landscape
Source
| Field |
Value |
| Title |
Charting the Landscape of Artificial Intelligence Ethics: A Bibliometric Analysis |
| Publisher |
International Journal of Digital Law and Governance (De Gruyter) |
| Author(s) |
Jiaxuan Qiu, Le Cheng, Jin Huang |
| Date |
2025-04-18 |
| URL |
https://www.degruyterbrill.com/document/doi/10.1515/ijdlg-2025-0007/html |
| Type |
Research paper (bibliometric analysis) |
| Origin |
Search-discovered |
Summary
| Dimension |
Rating |
| Reliability |
Medium |
| Relevance |
Medium |
| Bias: Missing data |
Some concerns |
| Bias: Measurement |
Low risk |
| Bias: Selective reporting |
Low risk |
| Bias: Randomization |
N/A — not an RCT |
| Bias: Protocol deviation |
N/A — not an RCT |
| Bias: COI/Funding |
Low risk |
Rationale
| Dimension |
Rationale |
| Reliability |
Medium — peer-reviewed journal article with rigorous bibliometric methodology (6,084 articles, 2015-2025, Web of Science). However, it examines AI ethics broadly rather than the specific safety-ethics divide. |
| Relevance |
Medium — does not directly measure safety-ethics homophily, but its finding that 94% of institutions are in the largest connected component provides a contrasting perspective on research community integration. |
| Bias flags |
Missing data concern: uses Web of Science rather than conference proceedings, which may underrepresent the ML/NLP conference-centric communities that Roytburg & Miller study. The two studies measure different populations. |
| Evidence ID |
Summary |
| SRC02-E01 |
94% of AI ethics institutions in largest connected component; 16 distinct keyword clusters |