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Large scale image classification models trained on top of popular datasetssuch as Imagenet have shown to have a distributional skew which leads todisparities in prediction accuracies across different subsections of populationdemographics. A lot of approaches have been made to solve for thisdistributional skew using methods that alter the model pre, post and duringtraining. We investigate one such approach - which uses a multi-label softmaxloss with cross-entropy as the loss function instead of a binary cross-entropyon a multi-label classification problem on the Inclusive Images dataset whichis a subset of the OpenImages V6 dataset. We use the MR2 dataset, whichcontains images of people with self-identified gender and race attributes toevaluate the fairness in the model outcomes and try to interpret the mistakesby looking at model activations and suggest possible fixes.

Understanding Gender and Racial Disparities in Image Recognition Models