arXiv AI
Explainability of Complex AI Models with Correlation Impact Ratio
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arXiv AI
Jul 17
Towards a Unified Multidimensional Explainability Metric: Evaluating Trustworthiness in AI Models
arXiv:2607. 14315v1 Announce Type: cross Abstract: In this paper, we present a comprehensive framework for assessing the explainability of various XAI methods, such as LIME and SHAP, across multiple datasets and machine learning models, with the ultimate goal of creating a unified multidimensional explainability score.
arXiv Machine Learning
Sep 18
Evaluating Explanation Methods by the Predictors They Induce
The paper proposes a new evaluation test for explanation methods: if an explanation accurately captures how a model uses its features, one should be able to reconstruct the model’s predictions from it. The authors convert explanations into predictors by summing feature effects and assess how well these predictors reproduce the model on unseen data, without any fitting. They apply this test to partial dependence plots, accumulated local effects, SHAP, and LIME across multiple datasets and model families, showing that the best method depends on feature dependence and that some existing quality metrics can favor flawed explanations.
arXiv AI
Aug 3
A Human-Centered Validation of the Explainability-Performance Coefficient
arXiv:2607. 29614v1 Announce Type: cross Abstract: The rapid adoption of deep learning models in high-risk domains has intensified the need for trustworthy Explainable Artificial Intelligence (XAI).
arXiv AI
Jul 17
IMEX Interaction-Based Model Explanation
arXiv:2607. 14096v1 Announce Type: new Abstract: In predictive modeling, the ability to explain why a model produces a given target prediction has become increasingly important [5, 10].
arXiv Machine Learning
Jun 16
Evaluating Local Explainability Metrics for Machine Learning Models on Tabular Data
arXiv:2605. 27618v2 Announce Type: replace Abstract: Despite the wide use of explainability techniques to attempt to understand the behavior of Artificial Intelligence (AI), the generated explanations may not always be reliable.
arXiv AI
Sep 16
A unified framework for global and local interpretability using adaptive derivative-ordered random explanation
The paper introduces Adaptive Derivative-Ordered Random Explanation (ADORE), a unified framework that uses first- and second-order derivatives to capture nonlinear feature interactions and feature-sample dynamics. ADORE combines global feature importance with local sample contributions, quantifying both magnitude and direction of feature impact while identifying critical samples. It achieves computational efficiency via randomized SVD and dynamic sparsity detection, outperforming LIME and SHAP across tabular, text, and image data, and is released as an open-source Python package on GitHub.