# Classification for everyone : Building geography agnostic models for fairer recognition

Source: [Classification for everyone : Building geography agnostic models for fairer recognition](<https://www.sota2.com/research/paper/68fa72c11e209131cea58668>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T06:32:05.519Z

Authors: Akshat Jindal, Shreya Singh, Soham Gadgil

Published: 2023-12-05T18:41:03.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2312.02957>)

[PDF](<https://arxiv.org/pdf/2312.02957.pdf>)