Derives a closed-form Shapley value for the squared robust Interval-Mahalanobis distance to explain variable contributions to outlyingness in interval-valued data.
Explainable artificial intelligence (XAI) 2.0: A manifesto of open challenges and interdisciplinary research directions
6 Pith papers cite this work. Polarity classification is still indexing.
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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.
NEURON integrates SNOMED CT, ML, and RAG LLM to raise AUC from 0.74-0.77 to 0.84-0.88 and human-aligned explainability scores from 0.50 to 0.85 on MIMIC-IV acute heart failure data.
Position paper proposing Model Science as a discipline to systematically analyze AI model behavior beyond benchmarks, drawing analogies from cognitive science, neuroscience, medicine, and agriculture.
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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Explainable Outlier Detection for Interval-valued Data
Derives a closed-form Shapley value for the squared robust Interval-Mahalanobis distance to explain variable contributions to outlyingness in interval-valued data.
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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 integrates SNOMED CT, ML, and RAG LLM to raise AUC from 0.74-0.77 to 0.84-0.88 and human-aligned explainability scores from 0.50 to 0.85 on MIMIC-IV acute heart failure data.
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The Case for Model Science: Verify, Explore, Steer, Refine
Position paper proposing Model Science as a discipline to systematically analyze AI model behavior beyond benchmarks, drawing analogies from cognitive science, neuroscience, medicine, and agriculture.
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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.