The paper introduces a straightforward evaluation method for explanation techniques: by converting each explanation into a predictor that sums the feature effects, the authors assess how accurately this predictor reproduces the original model’s predictions on unseen data. This approach applies to any explanation expressible as a function of features and is demonstrated on PDP, ALE, SHAP, and LIME. The authors theoretically show that summing partial dependence curves yields the optimal additive summary when features are independent, but this property fails with dependent features, and empirical results across diverse datasets confirm that the best-performing method depends on feature dependence.
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.
By Tom\'as Pereira, Jo\~ao Vitorino, Eva Maia, Isabel Pra\c{c}a
arXiv:2606. 06056v1 Announce Type: cross Abstract: Multiple machine learning models can achieve near-equivalent predictive performance on the same task, yet provide divergent feature-based explanations.
By Helge Spieker, J{\o}rn Eirik Betten, Arnaud Gotlieb
The paper introduces MCIR-M, a global feature‑importance method that accounts for feature dependence by conditioning each feature on its strongly dependent neighbours and computing a normalized Mutual Correlation Impact Ratio. MCIR-M yields a population score between 0 and 1, reaching zero when a feature is exactly conditionally redundant, and can be estimated with only a fraction of the data. Experiments on synthetic redundancy setups and the UCI HAR benchmark show that MCIR-M produces more stable rankings under near‑duplicate predictors compared to existing methods such as SHAP, SAGE, HSIC, and MI‑based scores.
By Poushali Sengupta, Sabita Maharjan, Frank Eliassen, Shashi Raj Pandey, Yan Zhang
The paper investigates the phenomenon of explanation multiplicity in SHAP, showing that SHAP explanations can vary significantly across repeated runs even when the model, input, and prediction remain unchanged. The authors introduce a dual‑seed evaluation protocol, a hierarchy of metrics, and null models to quantify this variability, finding that multiplicity is widespread across datasets, models, and sampling strategies. They also demonstrate that common metrics like L2 distance may underestimate instability, while rank‑based metrics reveal substantial changes in top‑ranked features, and that improved sampling methods do not fully eliminate run‑to‑run variation.
By Hyunseung Hwang, Seungeun Lee, Lucas Rosenblatt, Steven Euijong Whang, Julia Stoyanovich
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.
By Lemen Chao, Ming Lei, Anran Fanga
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).
By Christian Oliva, Luis F. Lago-Fern\'andez
The paper introduces ExCIR, a new explainability metric that addresses limitations of existing post‑hoc explainers by accurately handling correlated features and avoiding costly perturbations. ExCIR is theoretically grounded, computationally efficient, and remains stable under noise and sampling variations. Experiments on EEG, synthetic vehicular data, Digits, and Cats‑Dogs datasets show that ExCIR provides more interpretable feature explanations than current methods, and its information‑theoretic extension supports multi‑output and class‑conditioned explanations at scale.
By Poushali Sengupta, Rabindra Khadka, Sabita Maharjan, Frank Eliassen, Yan Zhang, Shashi Raj Pandey, Pedro G. Lind, Anis Yazidi
arXiv:2607. 08641v1 Announce Type: new Abstract: Over the last few years, there has been an increased interest in making machine learning models more interpretable.
By Yann Claes, Pierre Geurts, V\^an Anh Huynh-Thu
arXiv:2606. 03885v1 Announce Type: new Abstract: Feature attribution methods explain predictions by assigning importance scores to input features.
By Kieran A. Murphy, Shameen Shrestha
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.
By Georgios Makridis, Georgios Fatouros, Athanasios Kiourtis, Dimitrios Kotios, Vasileios Koukos, Dimosthenis Kyriazis, Jonh Soldatos
ProToMEx is a new explainability framework that uses Probabilistic Topic Models to learn latent topics representing high‑level reasons behind a classifier’s decisions, moving beyond simple feature attribution. It provides both global and local explanations, revealing multiple co‑existing reasons for individual predictions. Empirical results show that ProToMEx achieves comparable fidelity to SHAP and LIME while being 30–40× faster on standard tabular and synthetic datasets.
By Athina Georgara, Adarsh Valoor, Sarvapali D. Ramchurn