Datasheets for Datasets
Standardized documentation for ML datasets describing provenance, composition, collection methods, recommended use, and known limitations.
Datasheets for Datasets standardize ML dataset documentation – like nutrition labels for data, essential for bias audits and compliance.
Explanation
Inspired by datasheets in the electronics industry. Contains: Motivation, composition, collection process, preprocessing, usage recommendations, distribution, maintenance. Google calls them "Data Cards," Hugging Face integrates them as Dataset Cards.
Marketing Relevance
Foundation for responsible AI: Without dataset documentation, bias audits, reproducibility, and compliance are impossible.
Common Pitfalls
Datasheets often incomplete or outdated. No binding standard. Effort is underestimated. Datasheets exist but are not read.
Origin & History
Gebru et al. proposed Datasheets for Datasets in 2018. Google introduced Data Cards, Hugging Face standardized Dataset Cards. The EU AI Act requires comparable documentation for high-risk training data.
Comparisons & Differences
Datasheets for Datasets vs. Model Cards
Model Cards document the model (architecture, performance, bias); Datasheets document the dataset (provenance, composition, limitations).
Datasheets for Datasets vs. Data Governance
Data Governance is the process; Datasheets are the documentation artifact within that process.