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A structured documentation artifact detailing a dataset’s motivation, composition, collection, processing, recommended uses, and limitations.
DESCRIPTION
Datasheets for datasets provide standardized documentation to increase transparency and accountability in data use. They capture provenance, collection methods, consent, labeling processes, and known issues. By clarifying appropriate use and limitations, they prevent misuse and support ethical, compliant AI development. Datasheets complement model and system cards to complete lineage and governance.
ALTERNATIVE DEFINITIONS
Datasheets for Datasets
We propose datasheets for datasets to document the motivation, composition, collection process, recommended uses, and other key characteristics of datasets.
Introduces the concept as standardized dataset documentation across lifecycle characteristics.
Partnership on AI
ABOUT ML proposes documentation practices to improve transparency, including dataset documentation that outlines provenance, collection processes, intended uses, and limitations.
Places datasheets within a broader documentation ecosystem for ML transparency.
RELATED TERMS
Model Cards Data Discovery Platform Lineage Data Lineage Governance Data Quality Metadata Management Documentation
COMPETING TERMS
Data Card Data Statements
KNOWLEDGE AREAS
Data Management AI Governance Data Science Metadata Management
TARGET AUDIENCE
Data Steward Data Scientist Analytics Engineer Data Governance Manager AI Product Manager Data Engineer
EXTERNAL SOURCES
- Wikipedia - Datasheets for Datasets
- ArXiv - Original datasheets paper
- Google - Data Card Playbook guidance
- GitHub Docs - General dataset documentation practices
TOP QUESTIONS
What should a Datasheet for Datasets include?
Motivation, composition, collection, labeling, processing, distribution, maintenance, ethics, and recommended uses with known limitations.
Category: Requirements
How do Datasheets support compliance?
They document consent, provenance, and processing, enabling audits and privacy and governance checks.
Category: Compliance
Who is responsible for writing a Datasheet?
Data creators and stewards, with input from domain experts, legal, and privacy teams.
Category: Ownership
When should a Datasheet be created?
During dataset creation and updated on material changes, new versions, or discovered issues.
Category: Lifecycle
How do Datasheets relate to Model Cards?
Datasheets describe data sources; model cards describe model behavior. Together they enhance transparency and traceability.
Category: Relation
What are common pitfalls in Datasheets?
Incomplete provenance, vague intended uses, and missing subgroup coverage limit usefulness and can lead to misuse.
Category: Mistakes
How detailed should ethical considerations be?
Be explicit about risks, sensitive attributes, consent constraints, and prohibited uses with rationale.
Category: BestPractice
How do Datasheets help with bias mitigation?
They surface sampling strategies, labeling quality, and subgroup representation, enabling targeted evaluations and corrections.
Category: Benefits
Are Datasheets required by any standard?
Not mandated universally, but recommended by governance frameworks and increasingly expected by stakeholders.
Category: Standards