Datasheet for Datasets

Full definition

Datasheets sit beneath model cards in the documentation stack: a model card describes the trained artifact, while a datasheet describes the data it learned from. Critical fields include: who collected the data, what consents were obtained, demographic distributions, label sources and quality, and known biases. The EU AI Act's Article 10 sets the data-governance duties for high-risk systems, and Annex IV, point 2(d), asks for datasheets describing the training data, where relevant, in their technical documentation.

Why it matters

Most AI failures trace to data, not algorithms. A datasheet exposes the exact failure modes — coverage gaps, consent ambiguity, label noise — that downstream model cards inherit. Buyers should refuse training-data summaries and demand datasheets.

Example

A facial-recognition vendor publishes a datasheet revealing 86% of training images came from one continent; a procurement committee disqualifies the product on coverage grounds before pilot.

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Related terms

• Model Card
  A model card is a short, structured document that describes an AI model's intended use, performance, limitations, training data, evaluation results across demographic and operational subgroups, and known failure modes — the AI equivalent of a nutrition label.
• Training Data
  Training data is the curated dataset used to teach a machine-learning model — every pattern the model can recognize, every bias it inherits, and every limit to its accuracy ultimately traces back to this data.
• AI Bias
  AI bias is systematic, unfair difference in an AI system's outputs across demographic, geographic, or other groups — usually caused by biased training data, biased labels, or biased problem framing rather than the algorithm itself.
• AI Governance
  AI governance is the framework of policies, roles, controls, and processes an organization uses to ensure its AI systems are lawful, safe, fair, accountable, and aligned with business intent — across the full lifecycle from problem framing to retirement.

More governance & risk terms

• AI Risk Assessment
  An AI risk assessment is a structured review of an AI system's potential harms — to individuals, groups, the organization, and society — covering likelihood, severity, affected populations, and mitigation controls across safety, fairness, security, privacy, and compliance dimensions.
• NIST AI RMF
  The NIST AI Risk Management Framework (AI RMF 1.0, January 2023) is a voluntary U.S. framework for managing AI risks throughout the lifecycle, organized around four core functions — Govern, Map, Measure, Manage — and seven characteristics of trustworthy AI.
• Constitutional AI
  Constitutional AI (CAI) is a training technique, introduced by Anthropic in 2022, in which a model critiques and revises its own outputs against a written set of principles, then learns from those revisions instead of from human-labeled preferences alone.