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Modern machine learning systems such as image classifiers rely heavily onlarge scale data sets for training. Such data sets are costly to create, thusin practice a small number of freely available, open source data sets arewidely used. We suggest that examining the geo-diversity of open data sets iscritical before adopting a data set for use cases in the developing world. Weanalyze two large, publicly available image data sets to assess geo-diversityand find that these data sets appear to exhibit an observable amerocentric andeurocentric representation bias. Further, we analyze classifiers trained onthese data sets to assess the impact of these training distributions and findstrong differences in the relative performance on images from differentlocales. These results emphasize the need to ensure geo-representation whenconstructing data sets for use in the developing world.

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