Achieving Robust and Faithful Explanations via Conditional Pairwise Contrast
Bibliographic record
Abstract
Explainable Artificial Intelligence (XAI) is crucial for understanding complex models like deep neural networks, thereby enabling trust, debugging, fairness assessment, and regulatory compliance, particularly in high-stakes domains such as healthcare and manufacturing. Local Interpretable Model-agnostic Explanations (LIME) stands out as one of the most widespread techniques in this category. In multi-class classification settings, standard LIME generates a single “one-vs-all” explanation that fails to capture the accurate decision logic between competing classes, making its explanations unstable and low fidelity. This paper introduces CondLIME, an extension framework that resolves these issues by fundamentally reframing the local explanation task. CondLIME generates a series of pairwise conditional explanations, with each linear model focusing on the distinction between the predicted class and a specific competing class. These conditional models are then innovatively used as candidate splitting rules to construct an oblique decision tree. This tree acts as a high-fidelity and complete explanation, defining a precise and robust classification region. Its hierarchy indicates that CondLIME provides explanations incrementally with a more precise scope. Our experiments demonstrate that CondLIME significantly outperforms standard LIME, delivering more stable explanations with substantially higher local fidelity. By providing a more reliable and multi-faceted insight into a model's behavior, CondLIME enhances the trustworthiness of local explanations for critical applications.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".