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Reduced, Reused and Recycled: The Life of a Dataset in Machine Learning Research
Bernard Koch · Remi Denton · Alex Hanna · Jacob G Foster
Event URL: https://openreview.net/forum?id=zNQBIBKJRkd »

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.

Author Information

Bernard Koch (University of California, Los Angeles)
Remi Denton (Google)
Remi Denton

Remi Denton (they/them) is a Staff Research Scientist at Google, within the Technology, AI, Society, and Culture team, where they study the sociocultural impacts of AI technologies and conditions of AI development. Prior to joining Google, Remi received their PhD in Computer Science from the Courant Institute of Mathematical Sciences at New York University, where they focused on unsupervised learning and generative modeling of images and video. Prior to that, they received their BSc in Computer Science and Cognitive Science at the University of Toronto. Though trained formally as a computer scientist, Remi draws ideas and methods from multiple disciplines and is drawn towards highly interdisciplinary collaborations, in order to examine AI systems from a sociotechnical perspective. Remi’s recent research centers on emerging text- and image-based generative AI, with a focus on data considerations and representational harms. Remi published under the name "Emily Denton".

Alex Hanna (Google)
Jacob G Foster (University of California, Los Angeles)

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