Towards the Development of Explainable Machine Learning Models to Recognize the Faces of Autistic Children
Bibliographic record
Abstract
Machine learning with image classification has shown promise in supporting the detection ofautism in children, but the development of explainable models is still lacking. Thus, our studycompared the use of two algorithms to explain why facial images are categorized as autistic ornot. First, we trained and tested different models on the Autistic Children Facial Image Data Setto identify the one that produced the highest accuracy. Following the identification of the bestmodel, the analyses compared two methods to examine explainability: Local InterpretableModel-agnostic Explanations (LIME) and Randomized Input Sampling for Explanation of black-box models (RISE). Overall, the best model, ViT_Huge_14, produced an accuracy of 92% andLIME resulted in more explainable models than RISE. Albeit promising, researchers mustconduct further studies to examine the generalizability of the results prior to recommendingfacial image classification as a component of a multimethod approach to screening anddiagnosis.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".