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

Modern machine learning systems such as image classifers 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. Such strategies may be particularly important for ML applications in the developing world, where resources may be constrained and the cost of creating suitable large scale data sets may be a blocking factor. However, we suggest that examining the {\em geo-diversity} of open data sets is critical before adopting a data set for such use cases. In particular, we analyze two large, publicly available image data sets to assess geo-diversity and find that these data sets appear to exhibit a observable amerocentric and eurocentric representation bias. Further, we perform targeted analysis on classifiers that use these data sets as training data 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.