AINeutralarXiv – CS AI · 7h ago6/10
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A Practical Upper Bound on Selection Bias Effects in Medical Prediction Models
Researchers propose a novel upper bound method to assess how selection bias in training data impacts machine learning model performance when deployed to broader populations, addressing a critical gap in healthcare AI safety. The approach works with realistic constraints where the selection mechanism and target population are only partially observable, validated through synthetic and real-world medical datasets.