The work provides the first formal definitions of Rashomon sets for federated learning and introduces a multiplicity-aware training pipeline evaluated on standard benchmarks.
The curious case of arbitrariness in machine learning
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Updating clinical AI models can cause prediction flips, arbitrariness, and unfair error rates across groups, requiring dedicated monitoring dimensions.
citing papers explorer
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Rashomon Sets and Model Multiplicity in Federated Learning
The work provides the first formal definitions of Rashomon sets for federated learning and introduces a multiplicity-aware training pipeline evaluated on standard benchmarks.
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An empirical evaluation of the risks of AI model updates using clinical data: stability, arbitrariness, and fairness
Updating clinical AI models can cause prediction flips, arbitrariness, and unfair error rates across groups, requiring dedicated monitoring dimensions.