A Bibliometric Analysis of the Ethical and Social Implications of AI
Bibliographic record
Abstract
The rapid advancement of artificial intelligence (AI) has raised significant ethical and social concerns, necessitating a systematic analysis of research trends in this domain. This study employs a bibliometric analysis using data from Scopus and visualization through VOSviewer to map the scholarly landscape of AI ethics. The analysis identifies key research themes, including algorithmic bias, data privacy, transparency, accountability, and trust, while highlighting emerging topics such as ChatGPT, adversarial machine learning, AI in education and healthcare, and sustainability. The co-authorship and country collaboration networks reveal a highly interdisciplinary and globally connected research community, with strong contributions from the United States, Germany, India, and China, but limited representation from the Global South. Findings indicate that AI ethics research is evolving beyond theoretical discussions to address real-world applications and governance challenges. The study underscores the need for more inclusive AI policies, interdisciplinary collaborations, and ethical AI governance frameworks to ensure responsible AI development. Future research should focus on bridging the gap between AI engineering and ethical oversight, regulating AI-driven misinformation, and expanding the global diversity of AI ethics discourse.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.014 | 0.148 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".