Keywords: Benchmarks, AI Ethics, Algorithmic Fairness, Science of Science
TL;DR: Field-level analysis of dynamics of dataset (re)use revealing increasing concentration on fewer and fewer datasets introduced by a few elite institutions.
Abstract: 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.
Supplementary Material: pdf
Contribution Process Agreement: Yes
Dataset Url: https://github.com/kochbj/Reduced_Reused_Recycled
License: CC BY-SA 4.0
Author Statement: Yes