The Dataset Nutrition Label: A Framework to Drive Higher Data Quality Standards

Data provenance frameworks developed in AI/ML have incorporated perspectives from libraries and archives, feminist ethics, and the food industry.

“Data provenance frameworks developed in the context of AI and ML have incorporated perspectives from libraries and archives, feminist ethics and even the food industry (Holland et al. 2020;Jo and Gebru 2020;Luccioni et al. 2022).”

Traceability makes it possible to interpret and audit the history that precedes the output of ML algorithms.

“Traceability makes it possible to interpret and audit the history that precedes the output of ML algorithms [33]”

ML can become a tool for detecting smaller subsets of data that would most benefit from enrichment or for using modeling choices to mitigate bias.

“In cases like this, ML in itself can become a tool for detecting smaller subsets of data that would most benefit from enrichment or using modeling choices to mitigate bias [33,34].”

The requirement for contextual sensitivity is being met by the growing use of semantic standards that use ontologies and annotate data with graph-like properties.

“This requirement for contextual sensitivity is being met by the growing use of semantic standards that use ontologies and annotate data with graph-like properties [33].”

Big data remain necessary in many ML applications such as autonomous vehicles and clinical diagnosis.

“early advances in ML were motivated and, in some cases, enabled by the availability of big datasets, and big data remain necessary in many ML applications such as autonomous vehicles and clinical diagnosis [33,75].”

Data that are dispersed across multiple sources tend to have different schemas and approaches to storage and access.

“Data that are dispersed across multiple sources tend to have different schemas and approaches to storage and access [33,52].”