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We introduce a framework to measure how biases change before and afterfine-tuning a large scale visual recognition model for a downstream task. Deeplearning models trained on increasing amounts of data are known to encodesocietal biases. Many computer vision systems today rely on models typicallypretrained on large scale datasets. While bias mitigation techniques have beendeveloped for tuning models for downstream tasks, it is currently unclear whatare the effects of biases already encoded in a pretrained model. Our frameworkincorporates sets of canonical images representing individual and pairs ofconcepts to highlight changes in biases for an array of off-the-shelfpretrained models across model sizes, dataset sizes, and training objectives.Through our analyses, we find that (1) supervised models trained on datasetssuch as ImageNet-21k are more likely to retain their pretraining biasesregardless of the target dataset compared to self-supervised models. We alsofind that (2) models finetuned on larger scale datasets are more likely tointroduce new biased associations. Our results also suggest that (3) biases cantransfer to finetuned models and the finetuning objective and dataset canimpact the extent of transferred biases.

Variation of Gender Biases in Visual Recognition Models Before and After Finetuning