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AI documentation is useful only when you know what it describes. A dataset card documents data; a model card describes a trained model and its intended use; an evaluation report records a particular test; and a system card addresses a larger system that may combine models, software, and operational controls. An “agent card” may describe an agent’s tools and boundaries, but no generally accepted schema is established by the sources cited here.
None of these labels, by itself, proves that an AI system is safe, fair, or suitable for your use. Start with the artifact’s scope, then check its evidence, limits, version, and date.
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Which AI documentation artifact should you read?
Choose the document that matches the decision you need to make. The terms overlap in ordinary use, but their subjects differ. NTIA groups datasheets, model cards, and system cards among AI system disclosures, and distinguishes information about datasets from information about how a system processes an input. NTIA’s overview of AI system disclosures is a useful starting point.
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| Artifact | Main subject | Questions it can help answer | What it does not establish by itself |
|---|---|---|---|
| Dataset datasheet or data card | A dataset used in AI development or operation | Why was it created? What does it contain? How was it collected? What uses are recommended? | That its stated composition makes it representative of your deployment. |
| Model card | A trained model | What is its intended use? Which evaluations were run? How does performance vary across relevant conditions or groups? | That benchmark results transfer to another version, population, or setting. |
| Evaluation report | A model, system, or defined capability under a particular evaluation | What was tested, how, under what conditions, and with what result? What was not tested? | General reliability beyond the scope and design of the evaluation. |
| System card or system disclosure | A system combining models, software, policies, and operational processes | How does the system process inputs and produce outputs? What role does a model play? | That a model-level description also explains the full deployed system. |
| Agent card | An AI agent’s role, tools, capabilities, constraints, and operating context, where a particular format is proposed or used | What can it do? Which tools can it access? What boundaries and human oversight apply? | That “agent card” has one industry-wide definition or required field set. |
Google’s responsible AI guidance also identifies technical reports and model, data, and system cards as possible transparency artifacts for researchers, deployers, downstream developers, and end users. These documents can guide decisions, but they are not interchangeable. Google AI for Developers’ design guidance
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What should an AI model card include?
A model card is a document accompanying a trained model. The original 2019 proposal recommends reporting benchmarked evaluation across varied conditions, including relevant cultural, demographic, or phenotypic subgroups and intersections. Which groups matter depends on the application. Although the paper’s examples focus on human-centered computer-vision and NLP applications, it says the framework can be applied to any trained machine-learning model. “Model Cards for Model Reporting,” Google Research
Use the following as a reading checklist, not as a mandatory industry schema:
- Identity and currency: model name, version, publisher, date, and revision history.
- Intended use: applications the model is designed for, along with out-of-scope uses.
- Evaluation evidence: methods, datasets, metrics, and conditions used in testing.
- Relevant variation: performance across subgroups or contexts relevant to the application, where reported.
- Limitations and dependencies: known weaknesses, risks, and assumptions about how the model is used.
- Follow-up route: contact or feedback information, if provided.
Read intended use alongside evaluation results. A score has little meaning without the task, conditions, data, and relevant populations behind it. The model-card proposal’s central purpose is to clarify intended use and performance characteristics so users can avoid contexts for which a model is poorly suited.
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An evaluation report records what a particular test found about a defined model, system, capability, or risk. There is no single universal report template established by the cited material. NIST’s AI Resource Center provides resources on testing, evaluation, verification, and validation, and lists evaluation reports, including a pilot that combined expert-annotator and human-tester data. That pilot is an example, not a general requirement for every evaluation. NIST AI Resource Center
To judge whether findings support a decision, check whether the report lets you reconstruct the test:
- Scope: Was it a model, a full system, a specific capability, or a particular risk?
- Method: Was the work a benchmark, human test, expert review, red-team exercise, or another procedure?
- Data and conditions: What were the data sources and coverage? What environment, prompts, tools, and access limits applied?
- Metrics and findings: What was measured, and what do the reported results actually show?
- Limitations: Which risks, groups, or conditions were excluded or left untested? What uncertainty or reproducibility details are reported?
- Version and date: Which system snapshot was tested, and when was the report prepared or revised?
A result applies to the test as designed. A narrow benchmark, for example, cannot establish performance on untested tasks or in a different deployment environment.
What is the difference between a model card and a system card?
A model card focuses on the trained model: its intended use, evaluation, and limitations. A system card or disclosure addresses a broader operational boundary, which may include the model together with software, policies, and processes. That difference matters when deployment changes how a model is accessed, what tools or controls surround it, or how inputs become outputs.
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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteNTIA describes datasheets as covering a dataset’s motivation, composition, collection process, and recommended uses; it also notes that a system card can show how a system processes an input. Google’s guidance similarly treats model, data, and system cards as distinct possible transparency artifacts. A model-level card may therefore be informative without describing the behavior or controls of the complete deployed system.
What is an AI agent card?
“Agent card” is not established as a generally accepted standard by the cited primary sources. Treat the name as specific to the publisher or proposal, and check what that particular format means before comparing it with another document.
A useful agent description would help a reader identify the agent’s role, capabilities, accessible tools and permissions, constraints, operating conditions, and human-oversight arrangements. These are questions to ask, not a claim that every agent card follows one required field list. Google’s general guidance supports understandable system-level transparency but does not define an agent-card standard. Google AI for Developers’ design guidance
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How can you compare documentation from different publishers?
Compare like with like: first establish each document’s system boundary and intended decision, then compare the evidence within that boundary. These dimensions help expose differences that a label or headline score can conceal:
- Scope and boundary: dataset, model, evaluation exercise, agent, or complete system?
- Audience and decision: who is meant to use the document, and what decision should it inform?
- Methods and conditions: what evaluation was performed and under what circumstances?
- Data and population coverage: what data and groups are represented, and what is missing?
- Limits and exclusions: what risks or uses are outside the evidence?
- Version and date: does the documentation match the system version being considered?
NIST’s AI Risk Management Framework calls for documenting a system’s concept and objectives, assumptions, context of use, legal and regulatory requirements, and ethical considerations. The framework is intended for voluntary use. NIST identifies AI RMF 1.0 as released on January 26, 2023, and its resources page says the framework is being updated. NIST AI RMF 1.0 · NIST AI Risk Management Framework · NIST AI RMF resources
NIST’s current resources page also says the framework was developed over 18 months with contributions from more than 240 organizations; that figure describes framework development, not an AI system’s performance. NIST reported an initial public draft of “Guidance and Templates for Public-Facing AI Documentation: An AI Standards ‘Zero Draft’” on July 29, 2026, with comments considered through September 16, 2026. The cited page describes an initial draft, so check its current status before relying on it as final guidance. NIST standards update
Why do version and date matter?
Documentation can become stale as a model or system changes. Check that the artifact names the version evaluated and has a date or revision history, then compare it with the version you are actually considering. Google DeepMind maintains a live index of model cards spanning Gemini, generative models, robotics, and Gemma, with update dates at the model-card level. The index is not a substitute for checking the specific card relevant to your decision. Google DeepMind model cards
A card or report is evidence with boundaries: its usefulness depends on what it covers, how the evidence was produced, and whether its scope matches your intended use. Documentation can improve transparency and guide use; it does not, by itself, provide independent assurance or establish real-world safety, fairness, or suitability.
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