Model transparency is a quality of AI-enabled systems and machine-learning models.
Definition
Model transparency is the degree to which relevant stakeholders can obtain accurate, current, and usable information about an AI or machine-learning model’s identity, intended use, capabilities, limitations, architecture, provenance, training and evaluation data characteristics, evaluation results, version, license, and known risks.
The information should be sufficient to assess fitness for purpose, integrate the model responsibly, monitor model behavior, audit model-related decisions, and govern continued use.
Model transparency is related to, but not identical with, explainability. Transparency concerns the availability and usability of relevant information about a model; explainability concerns whether model behavior or outputs can be explained to an intended audience in a given context.
This definition is derived from the established standards and documentation practices below.
Related standards and practices
ISO/IEC 12792:2025 specifies a transparency taxonomy for AI systems. It defines information elements that help stakeholders identify and address transparency needs for AI systems.
ISO/IEC 12792:2025 - Transparency taxonomy of AI systems
NIST AI RMF treats documentation as part of AI risk management and states in Measure 2.9 that the AI model is explained, validated, and documented, and that AI system output is interpreted within its context to inform responsible use and governance.
NIST AI RMF Core - Measure 2.9
The EU AI Act requires providers of general-purpose AI models to keep technical documentation up to date and provide information that enables downstream AI-system providers to understand the model’s capabilities and limitations. Annex XII lists transparency information such as intended tasks, acceptable-use policies, release and distribution methods, architecture and parameters, input and output modalities, license, and information about training, testing, and validation data.
EU AI Act Article 53 and Annex XII
Model cards are a practical reporting format for model transparency. Mitchell et al. introduced model cards as short documents accompanying trained machine-learning models, including intended use, benchmarked evaluation, performance characteristics, and other relevant information.