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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A large randomized experiment finds that admissions officers' decisions remain largely unchanged when shown a more favorable algorithmic score from one of two similar models for the same applicant.
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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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Does Algorithmic Uncertainty Sway Human Experts? Evidence from a Field Experiment in Selective College Admissions
A large randomized experiment finds that admissions officers' decisions remain largely unchanged when shown a more favorable algorithmic score from one of two similar models for the same applicant.