TF-DM: Tool for Studying ML Model Resilience to Data Faults
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Machine learning (ML) is widely deployed in safety-critical systems (e.g. self-driving cars). Failures can have disastrous consequences in these systems, and hence ensuring the reliability of its operations is important. Mutation testing is a popular method for assessing the dependability of applications and tools have recently been developed for ML frameworks. However, the focus has been on improving the quality of test data. We present an open source data mutation tool, TensorFlow Data Mutator (TF-DM), which targets different kinds of data faults for any ML program written in TensorFlow 2. TF-DM supports different types of data mutators so users can study model resilience to data faults. We explain how different fault models are mapped to mutators in TF-DM, and present a detailed evaluation and resiliency analysis of 6 ML models and 3 datasets.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| 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