Position: Measure Dataset Diversity, Don't Just Claim It

Dora Zhao, Jerone T.A. Andrews, Orestis Papakyriakopoulos, Alice Xiang

Publikation: Beitrag in FachzeitschriftKonferenzartikelBegutachtung

Abstract

Machine learning (ML) datasets, often perceived as neutral, inherently encapsulate abstract and disputed social constructs.Dataset curators frequently employ value-laden terms such as diversity, bias, and quality to characterize datasets.Despite their prevalence, these terms lack clear definitions and validation.Our research explores the implications of this issue by analyzing “diversity” across 135 image and text datasets.Drawing from social sciences, we apply principles from measurement theory to identify considerations and offer recommendations for conceptualizing, operationalizing, and evaluating diversity in datasets.Our findings have broader implications for ML research, advocating for a more nuanced and precise approach to handling value-laden properties in dataset construction.

OriginalspracheEnglisch
Seiten (von - bis)60644-60673
Seitenumfang30
FachzeitschriftProceedings of Machine Learning Research
Jahrgang235
PublikationsstatusVeröffentlicht - 2024
Veranstaltung41st International Conference on Machine Learning, ICML 2024 - Vienna, Österreich
Dauer: 21 Juli 202427 Juli 2024

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