Governance
Model Card
A model card is a standardized documentation artifact for an AI model that describes its intended use cases, performance characteristics across demographic groups, training data sources, known limitations, and ethical considerations. Model cards were introduced by Google in 2018 and are now an emerging governance standard for both public and internal AI deployments.
Regulators and procurement teams increasingly require model cards for AI systems used in consequential decisions — hiring, lending, healthcare triage, and benefits eligibility. A model card is not a marketing document; it must disclose failure modes and out-of-distribution behavior even when that information is unflattering.
Related terms
- Explainable AI — Explainable AI (XAI) is the set of methods and practices that make an AI system's predictions understandable to humans — identifying which inputs drove which outputs.
- AI Bias — AI bias is systematic error in an AI system's outputs that produces unfair treatment of individuals or groups, typically arising from biased training data, biased labels, or model architecture choices that proxy for protected attributes.
- AI Governance — AI governance is the system of policies, controls, and accountabilities that determines what AI is allowed to do inside an organization, who approves AI deployments, how AI decisions are audited, and how risk is managed.
- Responsible AI — Responsible AI is an umbrella term for the operational practices that make AI deployments safe, fair, transparent, accountable, and aligned with human values — covering ethics, governance, security, privacy, and reliability across the full lifecycle.