Title: FINDING AND FIXING SPURIOUS PATTERNS WITH EXPLANATIONS. Abstract: Machine learning models often use spurious patterns such as "relying on the presence of a person to detect a tennis racket," which do not generalize.  In this work, we present an end-to-end pipeline for identifying and mitigating spurious patterns for image classifiers.   We start by finding patterns such as "the model's prediction for tennis racket changes 63% of the time if we hide the people."  Then, if a pattern is spurious, we mitigate it via a novel form of data augmentation.   We demonstrate that this approach identifies a diverse set of spurious patterns and that it mitigates them by producing a model that is both more accurate on a distribution where the spurious pattern is not helpful and more robust to distribution shift.