Integrating AI-Generated Images into Diversity, Equity, and Inclusion Research
Bibliographic record
Abstract
Research on diversity, equity, and inclusion (DEI) research in the workplace uses human images to explore biases related to gender, sexual orientation, and race. However, natural human images often lack standardization and fail to adequately represent diverse identities, limiting the reliability and generalizability of findings. Variability in image quality and contextual factors, such as lighting and background, further complicates the interpretation of results. This study addresses these methodological challenges by using Stable Diffusion, a deep learning model, to create AI-generated images as standardized stimuli for DEI research. These images were standardized for technical consistency—ensuring uniform lighting, resolution, and facial expressions—and evaluated for realism, likability, trustworthiness, and competence through a pilot survey with 500 participants. The final dataset comprised 20 images deemed inclusive and realistic. Results revealed no significant differences in participants’ evaluations of warmth, competence, and professionalism across AI generated photos, contributing the reliability of AI-generated images.
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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 | 0.014 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".