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M. University

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#artificial intelligence Preprint Oct 2026

Diagnosing Low-Confidence Patient Profiles via Localized Harmful Covariate Shift Detection

Before deploying clinical prediction models, their reliability must be assessed to ensure safe use. Errors may arise from distribution shifts between training and deployment or from heterogeneous performance where the model systematically underperforms for certain patient subgroups. Yet most post-hoc diagnostic methods...

Olivier Lefebvre, Hakima Laribi, D. Poenaru et al. · 0 citations
Preprint Aug 2026

The Overstated Cost of AI Fairness in Criminal Justice

It is shown that racial bias is not only present in the COMPAS dataset but is also amplified by the models trained on it, which undercuts both the assumption that algorithmic decision-making offers a neutral improvement over human judgment and the weaker claim that it merely mirrors preexisting human bias.

Ignacio Cofone, Warut Khern-am-nuai University of Oxford, M. University · 3 citations

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