  Standard empirical risk minimization (ERM) training can produce deep neural
network (DNN) models that are accurate on average but under-perform in
under-represented population subgroups, especially when there are imbalanced
group distributions in the long-tailed training data. Therefore, approaches
that improve the accuracy-group robustness trade-off frontier of a DNN model
(i.e. improving worst-group accuracy without sacrificing average accuracy, or
vice versa) is of crucial importance. Uncertainty-based active learning (AL)
can potentially improve the frontier by preferentially sampling
underrepresented subgroups to create a more balanced training dataset. However,
the quality of uncertainty estimates from modern DNNs tend to degrade in the
presence of spurious correlations and dataset bias, compromising the
effectiveness of AL for sampling tail groups. In this work, we propose
Introspective Self-play (ISP), a simple approach to improve the uncertainty
estimation of a deep neural network under dataset bias, by adding an auxiliary
introspection task requiring a model to predict the bias for each data point in
addition to the label. We show that ISP provably improves the bias-awareness of
the model representation and the resulting uncertainty estimates. On two
real-world tabular and language tasks, ISP serves as a simple "plug-in" for AL
model training, consistently improving both the tail-group sampling rate and
the final accuracy-fairness trade-off frontier of popular AL methods.
