Who does the fairness in health AI community represent?
Bibliographic record
Abstract
ABSTRACT OBJECTIVE Artificial intelligence (AI) and machine learning are central components of today’s medical environment. The fairness of AI, i.e. the ability of AI to be free from bias, has repeatedly come into question. This study investigates the diversity of the members of academia whose scholarship poses questions about the fairness of AI. METHODS The articles that combine the topics of fairness, artificial intelligence, and medicine were selected from Pubmed, Google Scholar, and Embase using keywords. Eligibility and data extraction from the articles were done manually and cross-checked by another author for accuracy. 375 articles were selected for further analysis, cleaned, and organized in Microsoft Excel; spatial diagrams were generated using Public Tableau. Additional graphs were generated using Matplotlib and Seaborn. The linear and logistic regressions were analyzed using Python. RESULTS We identified 375 eligible publications, including research and review articles concerning AI and fairness in healthcare. When looking at the demographics of all authors, out of 1984, 794 were female, and 1190 were male. Out of 375 first authors, 155 (41.33%) were female, and 220 (58.67%) were male. For last authors 110 (31.16%) were female, and 243 (68.84%) were male. In regards to ethnicity, 234 (62.40%) of the first authors were white, 103 (27.47%) were Asian, 24 (6.40%) were black, and 14 (3.73%) were Hispanic. For the last authors, 234 (66.29%) were white, 96 (27.20%) were Asian, 12 (3.40%) were black, and 11 (3.11%) were Hispanic. Most authors were from the USA, Canada, and the United Kingdom. The trend continued for the first and last authors of the articles. When looking at the general distribution, 1631 (82.2%) were based in high-income countries, 209 (10.5 %) were based in upper-middle-income countries, 135 (6.8%) were based in lower-middle-income countries, and 9 (0.5 %) were based in low-income countries. CONCLUSIONS Analysis of the bibliographic data revealed that there is an overrepresentation of white authors and male authors, especially in the roles of first and last author. The more male authors a paper had the more likely they were to be cited. Additionally, analysis showed that papers whose authors are based in higher-income countries were more likely to be cited more often and published in higher impact journals. These findings highlight the lack of diversity among the authors in the AI fairness community whose work gains the largest readership, potentially compromising the very impartiality that the AI fairness community is working towards.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.073 | 0.223 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.022 | 0.021 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".