FairLogue shows that intersectional disparities in two clinical prediction tasks are largely consistent with randomized group membership.
Fair and interpretable models for survival analysis
2 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 2representative citing papers
DMICF models interactions from user- and item-centric perspectives with a macro-micro prototype-aware variational encoder and dimension-wise intent alignment to improve collaborative filtering.
citing papers explorer
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FairLogue: Evaluating Intersectional Fairness across Clinical Machine Learning Use Cases using the All of Us Research Program
FairLogue shows that intersectional disparities in two clinical prediction tasks are largely consistent with randomized group membership.
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Dual-Perspective Disentangled Multi-Intent Alignment for Enhanced Collaborative Filtering
DMICF models interactions from user- and item-centric perspectives with a macro-micro prototype-aware variational encoder and dimension-wise intent alignment to improve collaborative filtering.