Designing diversity: ethical virtual agents for effective dermatological training
Bibliographic record
Abstract
This paper discusses the importance of diversity and inclusivity in designing virtual agent simulations for dermatology. As virtual reality (VR) technology become increasingly utilized in dermatological diagnosis, treatment, and training, there is a need to ensure the agent representations reflect diverse populations. Our paper explains that virtual agents should represent a wide range of ethnicities, genders, skin tones, and other physical characteristics relevant to dermatology. It criticizes current classification schemas like the Fitzpatrick scale as lacking diversity, and encourages alternative approaches. Technical considerations in modeling diverse agents are explored, with popular tools analyzed for customization options and usability. Ethical issues around cultural sensitivity and stereotyping are highlighted as crucial to agent design. Examples are provided of how skin conditions may manifest differently across diverse populations, emphasizing why inclusive agents are vital for virtual simulations. Overall, the paper argues that comprehensive agent diversity is indispensable for achieving acceptance of VR in dermatology and accurately connecting virtual representations to real-world patients.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".