Abstract
The machine learning community currently has no standardized process for
documenting datasets, which can lead to severe consequences in high-stakes
domains. To address this gap, we propose datasheets for datasets. In the
electronics industry, every component, no matter how simple or complex, is
accompanied with a datasheet that describes its operating characteristics, test
results, recommended uses, and other information. By analogy, we propose that
every dataset be accompanied with a datasheet that documents its motivation,
composition, collection process, recommended uses, and so on. Datasheets for
datasets will facilitate better communication between dataset creators and
dataset consumers, and encourage the machine learning community to prioritize
transparency and accountability.
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