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.

翻译：标准经验风险最小化（ERM）训练可产生平均精度较高但在代表性不足的群体子集上性能欠佳的深度神经网络（DNN）模型，尤其在存在长尾训练数据中群体分布不平衡的情况。因此，改善DNN模型的精度-群体鲁棒性权衡前沿（即在保持平均精度的情况下提升最差群体精度，或反之）的方法至关重要。基于不确定性的主动学习（AL）通过优先采样代表性不足的子集以构建更平衡的训练数据集，有可能改善该权衡前沿。然而，现代DNN在存在虚假相关性和数据集偏差时，其不确定性估计质量往往下降，从而削弱了AL在采样尾群体时的有效性。本文提出内省式自博弈（ISP）方法，这是一种通过添加辅助内省任务来改善深度神经网络在数据集偏差下不确定性估计的简单方法——该任务要求模型除预测标签外，还需预测每个数据点的偏差。我们证明ISP可显著提升模型表征的偏差感知能力及其产生的不确定性估计质量。在两项真实世界表格与语言任务中，ISP作为AL模型训练的简易"即插即用"模块，持续提升了流行AL方法的尾群体采样率及其最终的精度-公平性权衡前沿。