Bibliographic record
Abstract
This article provides an in-depth review of the concepts of interpretability and explainability in machine learning, which are two essential pillars for developing transparent, responsible, and trustworthy artificial intelligence (AI) systems. As algorithms become increasingly complex and are deployed in sensitive domains, the need for interpretability has grown. However, the ongoing confusion between interpretability and explainability has hindered the adoption of clear methodological frameworks. To address this conceptual ambiguity, we draw on the formal distinction introduced by Dib, which rigorously separates interpretability from explainability. Based on this foundation, we propose a revised classification of explanatory approaches structured around three complementary axes: intrinsic vs. extrinsic, specific vs. agnostic, and local vs. global. Unlike many existing typologies that are limited to a single dichotomy, our framework provides a unified perspective that facilitates the understanding, comparison, and selection of methods according to their application context. We illustrate these elements through an experiment on the Breast Cancer dataset, where several models are analyzed: some through their intrinsically interpretable characteristics (logistic regression, decision tree) and others using post hoc explainability techniques such as treeinterpreter for random forests. Additionally, the LIME method is applied even to interpretable models to assess the relevance and robustness of the locally generated explanations. This contribution aims to structure the field of explainable AI (XAI) more rigorously, supporting a reasoned, contextualized, and operational use of explanatory methods.
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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 | 0.035 | 0.098 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| 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".