No Classification without Representation: Assessing Geodiversity Issues in Open Data Sets for the Developing World

TL;DR

This paper highlights the importance of geo-diversity in open data sets for machine learning, revealing biases towards American and European regions and demonstrating how these biases affect classifier performance across different locales.

Contribution

It provides an analysis of geo-diversity biases in popular open image data sets and evaluates their impact on classifier performance in the developing world.

Findings

Data sets show amerocentric and eurocentric biases.

Classifier performance varies significantly across different geographic regions.

Highlighting the need for geo-representative data in machine learning applications.

Abstract

Modern machine learning systems such as image classifiers rely heavily on large scale data sets for training. Such data sets are costly to create, thus in practice a small number of freely available, open source data sets are widely used. We suggest that examining the geo-diversity of open data sets is critical before adopting a data set for use cases in the developing world. We analyze two large, publicly available image data sets to assess geo-diversity and find that these data sets appear to exhibit an observable amerocentric and eurocentric representation bias. Further, we analyze classifiers trained on these data sets to assess the impact of these training distributions and find strong differences in the relative performance on images from different locales. These results emphasize the need to ensure geo-representation when constructing data sets for use in the developing world.