Effect of AI system data modality on user trust
Bibliographic record
Abstract
The dataset comprises responses from optometrists who participated in a trust questionnaire. The questionnaire was designed to assess their perceptions and levels of trust when collaborating with two different types of AI systems—unimodal and multimodal—in the context of glaucoma diagnosis. The unimodal AI system relied solely on a single type of data, such as Fundus imaging, to provide diagnostic assistance, while the multimodal AI system utilized a combination of multiple data sources, potentially including Fundus imaging, patient history, and other diagnostic tests, to offer a more comprehensive analysis. The questionnaire gathered responses on several dimensions related to trust, including the optometrists' confidence in the AI systems’ input data, the accuracy of the AI-generated outputs, the explainability of the system’s recommendations, and the overall system quality. The optometrists were asked to evaluate both the unimodal and multimodal AI systems based on these criteria, providing insights into how different modalities of AI systems influence their trust, reliance, and decision-making during glaucoma diagnosis. The data collected offers valuable information for understanding AI technology in clinical practice.
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".