  Despite extensive efforts to create fairer machine learning (ML) datasets,
there remains a limited understanding of the practical aspects of dataset
curation. Drawing from interviews with 30 ML dataset curators, we present a
comprehensive taxonomy of the challenges and trade-offs encountered throughout
the dataset curation lifecycle. Our findings underscore overarching issues
within the broader fairness landscape that impact data curation. We conclude
with recommendations aimed at fostering systemic changes to better facilitate
fair dataset curation practices.
