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Revisiting Explainable AI through Model-Independent Concept Dictionaries
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arXiv Machine Learning
Oct 8
Revisiting Explainable AI through Model-Independent Concept Dictionaries
arXiv:2610.10301v1 Announce Type: new Abstract: Modern applications of AI rely on increasingly complex models. Explainable AI (XAI) has emerged as a set of techniques aimed at improving model transpa...
arXiv AI
Jul 28
KANEx: Translating Kolmogorov-Arnold Networks' Interpretability to Medical Explainability
arXiv:2607. 24730v1 Announce Type: cross Abstract: Computer vision models have become highly effective for medical applications, yet their black-box nature continues to undermine clinician trust.
arXiv Machine Learning
Oct 8
Beyond Explanation: Debugging Medical Imaging Models via Concept Intervention
The paper presents a plug‑and‑play framework that uses concept-based interpretation to debug medical imaging models. By aligning a single‑modality encoder with BioMedCLIP, the authors build a Concept Bottleneck Model that allows concept‑level interventions to distinguish causal from spurious features. Experiments on Mayo Clinic ultrasound and CheXpert chest X‑ray data show that these interventions can reliably diagnose model issues and even improve performance through guided fine‑tuning.
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 7
SMILE: Self-Explainable Multimodal Information Bottleneck for Medical Diagnosis
The paper introduces SMILE, a self‑explainable multimodal information bottleneck framework for medical diagnosis. It jointly optimizes predictive accuracy and modality‑specific explainability by selecting the most informative elements within each data modality. Experiments on diverse medical datasets show strong diagnostic performance, including a 9.1‑percentage‑point accuracy gain on the iCTCF dataset, and provide transparent, modality‑aware explanations that enhance both explainability and generalization.
arXiv AI
Sep 28
A Synthetic Ground-Truth Framework for the Evaluation of Explainable AI Methods
The paper introduces a synthetic ground‑truth framework for evaluating explainable AI (XAI) methods, addressing the lack of reliable evaluation procedures. By using controlled interventions to create datasets where the importance of input components is known, the framework generates ground‑truth explanations that align with the model’s actual decision process. The authors apply this approach to binary images, tabular data, and time series, and find that nine popular XAI methods exhibit significant limitations, underscoring the need for intervention‑based benchmarks.