Expanding the historical training window can degrade model performance and fairness under concept shift, with the harm appearing mainly when training data are large.
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When the Past Misleads: Rethinking Training Data Expansion Under Temporal Distribution Shifts
Expanding the historical training window can degrade model performance and fairness under concept shift, with the harm appearing mainly when training data are large.