Structured dataset documentation shows little engagement with major reflexivity themes from FAccT literature, leading to a new codebook and extended datasheet questions.
Title resolution pending
5 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
citation-role summary
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dataset 1
citation-polarity summary
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background 2representative citing papers
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.