Bernard Koch, Emily Denton, Alex Hanna and Jacob Foster won a best paper award, for Reduced, Reused and Recycled: The Life of a Dataset in Machine Learning Research, in the datasets and benchmarks track at NeurIPS 2021. Here, Bernard tells us about the advantages and disadvantages of benchmarking, the findings of their paper, and plans for future work. Machine learning is a rather unusual science, partly because it straddles the space between science and engineering. The main way that progress is evaluated is through state-of-the-art benchmarking. The scientific community agrees on a shared problem, they pick a dataset which they think is representative of the data that you might see when you try to solve that problem in the real world, then they compare their algorithms on a score for that dataset.

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.