SMX explains spectral ML classifiers by summarizing expert zones with PCA, testing quantile predicates via perturbation, aggregating via directed graph centrality, and reconstructing thresholds back onto original spectra.
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Explanation in artificial intelligence: Insights from the social sciences
14 Pith papers cite this work. Polarity classification is still indexing.
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A computational argumentation framework evaluates LLM summaries of parliamentary debates by checking preservation of formal argument structures tied to contested proposals.
A latent mediation framework with sparse autoencoders enables non-additive token-level influence attribution in LLMs by learning orthogonal features and back-propagating attributions.
EXTree structures ABAC policies as trees to enable efficient decisions and actionable explanations for denied access requests.
A qualitative study with 22 creative writers finds that the reflective value of AI refusals depends on alignment with users' situational thinking phases, cognitive beliefs, and views of AI roles.
Introduces a unified evaluation framework for XAI using five principled metrics and the PGCA method that fuses grid perturbation with Grad-CAM++ , reporting top scores in fidelity, interpretability and fairness on ResNet-50 models across five image domains.
The authors provide a detailed taxonomy of 21 risks associated with language models, covering discrimination, information leaks, misinformation, malicious applications, interaction harms, and societal impacts like job loss and environmental costs.
Explores value-level and graph-level aggregation in multi-agent value-based argumentation and proposes aggregating rankings extracted from attack relations as a third method.
The paper develops a design science framework for governing AI-assisted operational decision support in security operations centers by specifying a query-broker artifact that separates AI planning from execution through approved templates, policy validation, and engineering review gates.
Interval counterfactual explanations outperform point counterfactuals and feature importance scores in boosting model understanding and demonstrated trust according to a within-subjects user study.
Industry markets AI agents for orchestration, creation, and insight, but a usability study with 31 participants reveals users face challenges from capability misalignment and lack of meta-cognition in tools like Operator and Manus.
Empirical comparison shows gradient-based explanations for GNN node similarities are actionable, consistent, and retain effects when sparsified, unlike mutual information explanations.
LLMs support decision prediction and rationale generation but lack evidence for genuine decision explanation, requiring stricter standards to avoid over-crediting.
citing papers explorer
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Spectral Model eXplainer: a chemically-grounded explainability framework for spectral-based machine learning models
SMX explains spectral ML classifiers by summarizing expert zones with PCA, testing quantile predicates via perturbation, aggregating via directed graph centrality, and reconstructing thresholds back onto original spectra.
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Evaluating LLM-Driven Summarisation of Parliamentary Debates with Computational Argumentation
A computational argumentation framework evaluates LLM summaries of parliamentary debates by checking preservation of formal argument structures tied to contested proposals.
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Correcting Influence: Unboxing LLM Outputs with Orthogonal Latent Spaces
A latent mediation framework with sparse autoencoders enables non-additive token-level influence attribution in LLMs by learning orthogonal features and back-propagating attributions.
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EXTree: Towards Supporting Explainability in Attribute-based Access Control
EXTree structures ABAC policies as trees to enable efficient decisions and actionable explanations for denied access requests.
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Beyond Compliance: How AI Could Help Creative Writers by Refusing Them
A qualitative study with 22 creative writers finds that the reflective value of AI refusals depends on alignment with users' situational thinking phases, cognitive beliefs, and views of AI roles.
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A Unified Framework for Evaluating and Enhancing the Transparency of Explainable AI Methods via Perturbation-Gradient Consensus Attribution
Introduces a unified evaluation framework for XAI using five principled metrics and the PGCA method that fuses grid perturbation with Grad-CAM++ , reporting top scores in fidelity, interpretability and fairness on ResNet-50 models across five image domains.
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Ethical and social risks of harm from Language Models
The authors provide a detailed taxonomy of 21 risks associated with language models, covering discrimination, information leaks, misinformation, malicious applications, interaction harms, and societal impacts like job loss and environmental costs.
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Aggregation in Value-Based Argumentation Frameworks
Explores value-level and graph-level aggregation in multi-agent value-based argumentation and proposes aggregating rankings extracted from attack relations as a third method.
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Governing AI-Assisted Security Operations: A Design Science Framework for Operational Decision Support
The paper develops a design science framework for governing AI-assisted operational decision support in security operations centers by specifying a query-broker artifact that separates AI planning from execution through approved templates, policy validation, and engineering review gates.
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Improving understanding and trust in AI: How users benefit from interval-based counterfactual explanations
Interval counterfactual explanations outperform point counterfactuals and feature importance scores in boosting model understanding and demonstrated trust according to a within-subjects user study.
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Why Johnny Can't Use Agents: Industry Aspirations vs. User Realities with AI Agents
Industry markets AI agents for orchestration, creation, and insight, but a usability study with 31 participants reveals users face challenges from capability misalignment and lack of meta-cognition in tools like Operator and Manus.
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Explaining Graph Neural Networks for Node Similarity on Graphs
Empirical comparison shows gradient-based explanations for GNN node similarities are actionable, consistent, and retain effects when sparsified, unlike mutual information explanations.
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LLMs Should Not Yet Be Credited with Decision Explanation
LLMs support decision prediction and rationale generation but lack evidence for genuine decision explanation, requiring stricter standards to avoid over-crediting.
- Beyond Explainable AI (XAI): An Overdue Paradigm Shift and Post-XAI Research Directions