arXiv:2608. 05702v1 Announce Type: new Abstract: Scientific machine learning commonly validates models at the level of a subdomain, a benchmark split, or an explanation for one prediction.
By Gnankan Landry Regis N'guessan, Bum Jun Kim
arXiv:2606. 26228v1 Announce Type: cross Abstract: We review the concepts of interpretability and explainability as they apply to machine learning in physics.
By Rikab Gambhir, Luisa Lucie-Smith, Jesse Thaler
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 a formal auditing framework to evaluate the robustness and fidelity of post‑hoc explainers such as SHAP and LIME. It defines a Trust Score that combines how stable an explanation is under small input perturbations with how well the highlighted features actually influence the model’s prediction. Experiments on a Madagascar malnutrition dataset show that even highly accurate models can produce unreliable explanations, and that fidelity scores degrade when models overfit.
By Rosa Elysabeth Ralinirina, Jean Christian Ralaivao, Niaiko Micha\"el Ralaivao, Alain Josu\'e Ratovondrahona, Thomas Mahatody
arXiv:2606. 08532v5 Announce Type: replace Abstract: Modern artificial intelligence excels at prediction but cannot explain.
By Lei Lin, Xinlong Pan, Ronghao Wang, Chunbao Zhou, Jue Wang, Yangang Wang, Ivana Rasovska
arXiv:2603. 14894v3 Announce Type: replace-cross Abstract: Trust and ethical concerns due to the widespread deployment of opaque machine learning (ML) models motivating the need for reliable model explanations.
By Sumedha Chugh, Ranjitha Prasad, Nazreen Shah