Machine learning models rely heavily on their training datasets, inheriting inherent biases and limitations. Yet unlike other fields like electronics where datasheets meticulously detail components' operating parameters, no standards exist for documenting machine learning datasets. This research proposes "datasheets for datasets" to fill that gap, increasing transparency and mitigating risks.


Datasets impart significant yet often opaque influences on models. Biased data produces biased AI, like résumé screening tools that disadvantaged women. Undocumented datasets also limit reproducibility. The World Economic Forum thus recommends documenting datasets to avoid discrimination.

The authors devised an initial set of questions for dataset creators to reflect on a dataset's motivation, composition, collection, processing, uses, distribution and maintenance. For example, what subpopulations does the data represent? Could it directly or indirectly identify individuals? Might it result in unfair treatment for certain groups?


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