Extracting Decision Paths via Surrogate Modeling for Explainability of Black Box Classifiers
Bibliographic record
Abstract
A common challenge in using intricate machine learning (ML) classifiers in critical domains is the lack of transparency in making predictions despite exhibiting high performance. Besides, relying solely on a single ML model may introduce uncertainties due to each algorithm's distinct strengths and weaknesses in classification. In this study, we propose a novel method that collects explanations derived from multiple ML classifiers, and subsequently, through subset optimization, extracts a high-quality explanation represented as a set of rules in disjunctive normal form—referred to as decision paths. Quality check of the shortlisted explanation is done considering accuracy of the underlying ML model, fidelity, confidence, instance coverage, and interpretability. We applied our method to a large and complicated real-life dataset related to kidney transplants, addressing a binary classification problem. The experiments show that our method optimally balances the reliability and coverage of the explanation, while minimizing its complexity.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".