FIFA: Making Fairness More Generalizable in Classifiers Trained on Imbalanced Data. (arXiv:2206.02792v1 [cs.LG])

Algorithmic fairness plays an important role in machine learning and imposing
fairness constraints during learning is a common approach. However, many
datasets are imbalanced in certain label classes (e.g. "healthy") and sensitive
subgroups (e.g. "older patients"). Empirically, this imbalance leads to a lack
of generalizability not only of classification, but also of fairness
properties, especially in over-parameterized models. For example,
fairness-aware training may ensure equalized odds (EO) on the training data,
but EO is far from being satisfied on new …

arxiv classifiers data fairness fifa lg making