Understanding Gender and Racial Disparities in Image Recognition Models
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Large scale image classification models trained on top of popular datasets such as Imagenet have shown to have a distributional skew which leads to disparities in prediction accuracies across different subsections of population demographics. A lot of approaches have been made to solve for this distributional skew using methods that alter the model pre, post and during training. We investigate one such approach - which uses a multi-label softmax loss with cross-entropy as the loss function instead of a binary cross-entropy on a multi-label classification problem on the Inclusive Images dataset which is a subset of the OpenImages V6 dataset. We use the MR2 dataset, which contains images of people with self-identified gender and race attributes to evaluate the fairness in the model outcomes and try to interpret the mistakes by looking at model activations and suggest possible fixes.
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