Structured dataset documentation shows little engagement with major reflexivity themes from FAccT literature, leading to a new codebook and extended datasheet questions.
How Explainability Contributes to Trust in AI
5 Pith papers cite this work. Polarity classification is still indexing.
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A fairness-aware multi-group target detection approach reduces bias across groups and outperforms existing fairness-aware baselines in toxicity detection tasks.
Heterogeneous graph neural networks with post-hoc explanations improve accuracy on six land-use indicators from mobility data and provide feature attribution and counterfactual insights aligned with commuting patterns.
DGMM is proposed as an explicit graph-structured memory architecture for AI that enables persistent episodic memory, cue-based recall, and context-dependent interpretation without retraining.
A scoping review of AIES and FAccT literature concludes that AI trustworthiness research prioritizes technical precision over social, ethical, and institutional factors, leaving the sociotechnical nature of AI systems underexplored.
citing papers explorer
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Evaluating Structured Documentation as a Tool for Reflexivity in Dataset Development
Structured dataset documentation shows little engagement with major reflexivity themes from FAccT literature, leading to a new codebook and extended datasheet questions.
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Fairness-Aware Multi-Group Target Detection in Online Discussion
A fairness-aware multi-group target detection approach reduces bias across groups and outperforms existing fairness-aware baselines in toxicity detection tasks.
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Heterogeneous Graph Neural Networks with Post-hoc Explanations for Multi-modal and Explainable Land Use Inference
Heterogeneous graph neural networks with post-hoc explanations improve accuracy on six land-use indicators from mobility data and provide feature attribution and counterfactual insights aligned with commuting patterns.
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The Dynamic Gist-Based Memory Model (DGMM): A Memory-Centric Architecture for Artificial Intelligence
DGMM is proposed as an explicit graph-structured memory architecture for AI that enables persistent episodic memory, cue-based recall, and context-dependent interpretation without retraining.
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Understanding AI Trustworthiness: A Scoping Review of AIES & FAccT Articles
A scoping review of AIES and FAccT literature concludes that AI trustworthiness research prioritizes technical precision over social, ethical, and institutional factors, leaving the sociotechnical nature of AI systems underexplored.