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Using transfer learning to adapt a pre-trained "source model" to a downstream"target task" can dramatically increase performance with seemingly no downside.In this work, we demonstrate that there can exist a downside after all: biastransfer, or the tendency for biases of the source model to persist even afteradapting the model to the target class. Through a combination of synthetic andnatural experiments, we show that bias transfer both (a) arises in realisticsettings (such as when pre-training on ImageNet or other standard datasets) and(b) can occur even when the target dataset is explicitly de-biased. Astransfer-learned models are increasingly deployed in the real world, our workhighlights the importance of understanding the limitations of pre-trainedsource models. Code is available at https://github.com/MadryLab/bias-transfer

When does Bias Transfer in Transfer Learning?