  In this paper, we analyze different methods to mitigate inherent geographical
biases present in state of the art image classification models. We first
quantitatively present this bias in two datasets - The Dollar Street Dataset
and ImageNet, using images with location information. We then present different
methods which can be employed to reduce this bias. Finally, we analyze the
effectiveness of the different techniques on making these models more robust to
geographical locations of the images.
