2021/12/03 by Bernard Koch, Emily Denton, Koch, Bernard +5 · 2 voices · 11 citations
Computer Science · Mathematics · #Computation and Language (cs.CL) #Computer Vision and Pattern Recognition (cs.CV) #Computers and Society (cs.CY) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #cs.CL #cs.CV #cs.CY #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.2112.01716
35th Conference on Neural Information Processing Systems (NeurIPS 2021), Sydney, Australia
arxiv created 2021/12/03 · arxiv published 2021/12/03 · arxiv updated 2021/12/06
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.