Calibrating a Transformer-Based Model’s Confidence on Community-Engaged Research Studies: Decision Support Evaluation Study
Bibliographic record
Abstract
BACKGROUND: Deep learning offers great benefits in classification tasks such as medical imaging diagnostics or stock trading, especially when compared with human-level performances, and can be a viable option for classifying distinct levels within community-engaged research (CEnR). CEnR is a collaborative approach between academics and community partners with the aim of conducting research that is relevant to community needs while incorporating diverse forms of expertise. In the field of deep learning and artificial intelligence (AI), training multiple models to obtain the highest validation accuracy is common practice; however, it can overfit toward that specific data set and not generalize well to a real-world population, which creates issues of bias and potentially dangerous algorithmic decisions. Consequently, if we plan on automating human decision-making, there is a need for creating techniques and exhaustive evaluative processes for these powerful unexplainable models to ensure that we do not incorporate and blindly trust poor AI models to make real-world decisions. OBJECTIVE: We aimed to conduct an evaluation study to see whether our most accurate transformer-based models derived from previous studies could emulate our own classification spectrum for tracking CEnR studies as well as whether the use of calibrated confidence scores was meaningful. METHODS: We compared the results from 3 domain experts, who classified a sample of 45 studies derived from our university's institutional review board database, with those from 3 previously trained transformer-based models, as well as investigated whether calibrated confidence scores can be a viable technique for using AI in a support role for complex decision-making systems. RESULTS: Our findings reveal that certain models exhibit an overestimation of their performance through high confidence scores, despite not achieving the highest validation accuracy. CONCLUSIONS: Future studies should be conducted with larger sample sizes to generalize the results more effectively. Although our study addresses the concerns of bias and overfitting in deep learning models, there is a need to further explore methods that allow domain experts to trust our models more. The use of a calibrated confidence score can be a misleading metric when determining our AI model's level of competency.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.169 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.011 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.008 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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; both teacher heads agree on what is shown here.
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".