  We study utilizing auxiliary information in training data to improve the
trustworthiness of machine learning models. Specifically, in the context of
image classification, we propose to optimize a training objective that
incorporates bounding box information, which is available in many image
classification datasets. Preliminary experimental results show that the
proposed algorithm achieves better performance in accuracy, robustness, and
interpretability compared with baselines.
