A Novel Method to Estimate Measurement Error in AI-Assisted Measurements
Why this work is in the frame
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Bibliographic record
Abstract
Adopting Artificial Intelligence (AI) systems in measurement instruments and systems entails a necessity to predict the error contributed by the AI model to the measured value, especially on out-of-sample data. However, reporting aggregated error estimates, such as model accuracy or Root Mean Square Error (RMSE) as is customary in AI, cannot quantify the error of the AI model for a single measurement instance, which is what we need in measurement. In this paper, we propose a novel method to estimate the AI model’s error for a single measurement. Our goal is to predict the error and use it to correct the predicted measurand’s quantity. To do so, in the first step we use an existing dataset to train an AI model that predicts measurement values, which is the usual approach for designing AI-assisted measurement systems. Our contribution is in the second step, where we create a secondary dataset that consists of the errors between the ground truth and the predicted values, and we use this dataset to train a secondary AI model that predicts the errors. We then adjust the predicted measurement values with the predicted error values. Our performance evaluations on the well-known California Housing dataset shows that our approach lowers the measurement predictions’ Mean Absolute Percentage Error from 21% to 16%, resulting in more accurate measurements.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it