The authors introduce a taxonomy with target, functional role, and mode of justification axes plus a framework that decomposes abstract XAI desiderata into concrete benchmarkable tasks via identified dependency structures.
Explainable artificial intelligence (XAI) 2.0: A manifesto of open challenges and interdisciplinary research directions
4 Pith papers cite this work. Polarity classification is still indexing.
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NEURON raises AUC from 0.74-0.77 to 0.84-0.88 on MIMIC-IV heart-failure mortality prediction while lifting human-aligned explanation scores from 0.50 to 0.85 by grounding SHAP values in SNOMED CT and patient notes via RAG-LLM.
A qualitative-to-quantitative scoring framework is proposed to evaluate how well model-agnostic XAI methods support EU AI Act explainability requirements.
A survey proposing a taxonomy of XAI techniques for food quality research organized by data types and explanation methods.
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Bridging the Disciplinary Gap in Explainable AI: From Abstract Desiderata to Concrete Tasks
The authors introduce a taxonomy with target, functional role, and mode of justification axes plus a framework that decomposes abstract XAI desiderata into concrete benchmarkable tasks via identified dependency structures.
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NEURON: A Neuro-symbolic System for Grounded Clinical Explainability
NEURON raises AUC from 0.74-0.77 to 0.84-0.88 on MIMIC-IV heart-failure mortality prediction while lifting human-aligned explanation scores from 0.50 to 0.85 by grounding SHAP values in SNOMED CT and patient notes via RAG-LLM.
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Assessing Model-Agnostic XAI Methods against EU AI Act Explainability Requirements
A qualitative-to-quantitative scoring framework is proposed to evaluate how well model-agnostic XAI methods support EU AI Act explainability requirements.
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Explainable Artificial Intelligence Techniques for Interpretation of Food Models: a Review
A survey proposing a taxonomy of XAI techniques for food quality research organized by data types and explanation methods.