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HXAI: Hierarchical Privacy-Preserving Explainable AI in Distributed Energy Systems
Researchers propose HXAI, a hierarchical privacy-preserving explainable AI framework for distributed energy systems that enables grid operators to obtain decision-relevant insights without accessing household-level consumption data.
Oct 5, 2026
Assessing Alignment and Stability of Feature Importance Explanations via Weight of Evidence
A new arXiv preprint introduces a hypothesis-testing framework using Weight of Evidence (WoE) to evaluate how well feature importance methods (FIMs) align with domain knowledge or ground truth and how stable they are across perturbations.
Sep 2, 2026
Position: Explainability Research Must Prioritize Foundations over Ad-hoc Methods
A position paper argues that Explainable AI (XAI) research must shift from producing isolated explanation methods to solving foundational problems—like ill-defined objectives, weak evaluation frameworks, and missing human-in-the-loop feedback pipelines—to enable real-world impact.
Jul 18, 2026
From ML Predictions to Informed Diagnostic Assistance Using the Toulmin Model of Argumentation
Researchers propose a framework that structures AI-generated medical diagnoses using the Toulmin model of argumentation to improve interpretability and human oversight in retinal diagnosis.
Jul 14, 2026