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#retraining

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papersSEP 10 04:00 UTC

Study Compares Retraining Policies for Subgroup Disparity Under Data Drift

A new arXiv paper examines how the choice of retraining policy affects subgroup error rates in deployed classifiers as data distributions drift. The authors run paired comparisons of complete scheduled retraining against loss-triggered and subgroup-gap-triggered approaches, tracking cumulative subgroup disparity across model sequences, including gaps between updates. The work frames retraining timing as a question of fairness measurement rather than accuracy alone.

fairness/retraining/distribution-shift/evaluation