Datasheets for Datasets.¶
Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., III, H. D., & Crawford, K. (2021). Datasheets for Datasets. Communications of the ACM, 64(12), 86-92.
Cited by¶
4 citations across 4 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Boundary Disclosure Card
- Across these substrates the structural force is the same: when the producer-consumer interface is wide and reuse is impersonal, a standardized disclosure attached to the boundary substitutes for the trust, inspection, or shared context the consumer cannot otherwise obtain — and the fact that ML data cards explicitly borrowed the food-label pattern is direct evidence of the structure's portability.
This sourceProposes dataset documentation explicitly borrowing the electronics-datasheet and consumer-label idea for datasets and models.
- Across these substrates the structural force is the same: when the producer-consumer interface is wide and reuse is impersonal, a standardized disclosure attached to the boundary substitutes for the trust, inspection, or shared context the consumer cannot otherwise obtain — and the fact that ML data cards explicitly borrowed the food-label pattern is direct evidence of the structure's portability.
- Information Use License
- Remedy varies wildly and the propagation setting varies deliberately; what does not vary is that a party already holding the information is told which of its acts are licit.
This sourceProposes documentation travelling with a dataset that records recommended and out-of-scope uses for the party already holding it.
- Remedy varies wildly and the propagation setting varies deliberately; what does not vary is that a party already holding the information is told which of its acts are licit.
- Reference Standard Decay
- The metrology discipline of explicit reference traceability and versioning is moving into ML benchmark practice as datasheets for datasets, versioned benchmarks, and reference-card requirements — a direct port of the role-asymmetry insight from instruments to label sets.
This sourceImports metrology's traceability-and-versioning discipline into ML, documenting dataset/benchmark provenance and revision.
- The metrology discipline of explicit reference traceability and versioning is moving into ML benchmark practice as datasheets for datasets, versioned benchmarks, and reference-card requirements — a direct port of the role-asymmetry insight from instruments to label sets.
- Rhetorical Velocity
- Machine-learning training data: content authored with awareness that it will be ingested and re-emitted by models, with provenance markers and explicit licensing as recomposition-aware moves.
This sourceProposes documenting a dataset's provenance, composition, intended uses, and distribution so creators and downstream re-users (including ML training pipelines) can reason about recomposition and re-emission — a provenance-and-licensing layer for content authored to be ingested.
- Machine-learning training data: content authored with awareness that it will be ingested and re-emitted by models, with provenance markers and explicit licensing as recomposition-aware moves.
Verification¶
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