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Found 3 papers, 1 papers with code
Fairness Hub Technical Briefs: Definition and Detection of Distribution Shift
Distribution shift is a common situation in machine learning tasks, where the data used for training a model is different from the data the model is applied to in the real world.
Fairness Hub Technical Briefs: AUC Gap
To measure bias, we encourage teams to consider using AUC Gap: the absolute difference between the highest and lowest test AUC for subgroups (e. g., gender, race, SES, prior knowledge).
Cross-Institutional Transfer Learning for Educational Models: Implications for Model Performance, Fairness, and Equity
We also find that stacked ensembling provides no additional benefits to overall performance or fairness compared to either a local model or the zero-shot transfer procedure we tested.
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