This survey examines explainable AI (XAI) in wireless PHY layers, formalizing goals, taxonomy, and applications.

The XAIxArts workshop explores the operationalisation of Explainable AI in the Arts, focusing on diversity, ideation, and resource development.

The paper proposes Faithful Agentic XAI (FAX), a verification framework that explicitly checks LLM-generated explanations against model behavior, significantly improving explanation faithfulness on a…

This paper critically reviews the intersection of philosophy of science and explainable AI (XAI) in medicine, identifying necessary conditions for a philosophically grounded approach to explanation.

The paper proposes ExAI5G, a logic-based explainable AI framework that integrates a Transformer-based IDS with XAI techniques to provide highly accurate and transparent intrusion detection for 5G netw…

This paper reports early findings from a study investigating how explainability requirements are elicited, specified, and validated using established RE techniques in an industrial context.

This paper explores the application of explainability techniques to Reinforcement Learning algorithms in Air Traffic Control using a simplified environment and a saliency map.

The authors conducted a user study to assess the effectiveness of their interactive visual query tracer and builder tools for Nemo, a Datalog reasoner, in helping students learn Datalog.

The paper introduces eXTC, a novel framework that combines structured prompt optimization, knowledge distillation, and reinforcement learning to create a highly performant and fully interpretable text…

The paper introduces TELL, a novel explainable AI-generated text detection architecture that provides detailed, human-understandable explanations for its scores, achieving competitive performance whil…

This paper presents TypoNet, a system that constructs and validates a symbolic model of a production-scale WAN from network artifacts using large language models for translation and a solver for relia…

The paper compares verbalized feature attributions and self-generated rationales for explaining model behavior, finding that the format and granularity of the explanation significantly affect its abil…

This paper proposes a Bi-level Neural Architecture Search (Bi-NAS) framework to optimize explanations in recommender systems, refining cross-attention mechanisms and feature interaction functions whil…

HypoAgent is an agentic framework that enables interactive, multi-turn abductive hypothesis generation over knowledge graphs, achieving state-of-the-art performance by integrating specialized agents f…

The paper introduces AIBuildAI-2, a knowledge-enhanced agent that significantly improves the automatic building of AI models by integrating an external, evolving knowledge system, achieving state-of-t…

This paper introduces EXTree, a novel structure for Attribute-based Access Control (ABAC) policies that optimizes for both fast evaluation and human-understandable explanations when access is denied.

This book provides a compact, derivation-oriented mathematical primer that connects major families of generative AI models, showing their underlying structural relationships.

The paper systematically evaluates concept-based explainability in MLLMs, finding that forcing models to generate formal explanations degrades predictive accuracy, suggesting that explaining is genuin…

MOOSE-Copilot is a novel web-based framework that unifies scientific hypothesis discovery by formalizing human-AI interaction, significantly improving performance over autonomous LLM baselines.