  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.
