  Benchmark datasets play a central role in the organization of machine
learning research. They coordinate researchers around shared research problems
and serve as a measure of progress towards shared goals. Despite the
foundational role of benchmarking practices in this field, relatively little
attention has been paid to the dynamics of benchmark dataset use and reuse,
within or across machine learning subcommunities. In this paper, we dig into
these dynamics. We study how dataset usage patterns differ across machine
learning subcommunities and across time from 2015-2020. We find increasing
concentration on fewer and fewer datasets within task communities, significant
adoption of datasets from other tasks, and concentration across the field on
datasets that have been introduced by researchers situated within a small
number of elite institutions. Our results have implications for scientific
evaluation, AI ethics, and equity/access within the field.
