Classification for everyone : Building geography agnostic models for fairer recognition
This work addresses fairness issues in AI for global users, presenting incremental improvements over existing bias mitigation techniques.
The paper tackles geographical bias in image classification models by analyzing and comparing methods to reduce it, achieving improved robustness across different locations.
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.