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About this paper
Francesca Panero, Ernst C. Wit, Marco Scutari
The authors assess uncertainty in fairness-constrained models with bootstrap resampling and Bayesian posteriors, not just a fitted fairness value. Bootstrap reflects sampling variability; posteriors condition on observed data. In simulated Bayesian linear regression, posterior fairness exceeds the target with probability greater than 0.5, with some values up to 50% above it; this finding is limited to that setting.
Summary by alphaXiv.
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