AI Model Metadata

architecture details, hyperparameters, training datasets, versioning, and performance metrics. Enables model governance and lifecycle management.

AI Model metadata

• Model details: Architecture (e.g., transformer, CNN), training dataset sources, hyperparameters, version numbers.
• Provenance: Who built it, when, and under what conditions.
• Evaluation metrics: Accuracy, F1 scores, fairness metrics, or bias checks.
• Usage constraints: Licenses, intended use cases, known limitations.

AI Model Metadata

• Architecture details (model type, size, parameters)
• Training data characteristics and sources
• Performance metrics and benchmarks
• Known limitations and biases
• Version information and update history

AI Data

Lineage & Provenance Tracks the origin, transformations, and flow of data through pipelines. Crucial for debugging and compliance.

for AI Datasets

• Descriptive metadata: Dataset name, creator, creation date, format, size.
• Provenance metadata: How and where the data was collected, preprocessing steps.
• Quality metadata: Labeling accuracy, bias detection results, completeness.
• Ethical metadata: Consent information, privacy considerations, or restrictions on use.

AI Training Metadata

• Dataset provenance and licensing
• Training hyperparameters and configurations
• Compute resources used
• Training duration and costs
• Validation and testing results

AI Inference Metadata

Captures runtime context: input sources, timestamps, model version used, confidence scores, and post-processing steps.

for AI Generated Content

• Provenance signals: Indicators that content (e.g., text, images, audio) was AI-generated—sometimes embedded as hidden tags or watermarks.
• Usage tracking: When, where, and how an AI system generated or altered the content.
• Attribution: Links back to the model or service that produced the output.

AI Inference Metadata - Information generated during AI system usage:

• Input/output timestamps
• Confidence scores and uncertainty measures
• Processing time and resource consumption
• Model version used for specific predictions

AI Feature Metadata

Describes feature types (categorical, continuous), encoding strategies, statistical properties, and relationships. Supports feature engineering and drift detection.

in AI Governance and Compliance

• Organizations and standards bodies (like NIST or ISO) treat AI metadata as essential for:
• Transparency Auditing AI decisions and ensuring accountability.
• Reproducibility Letting others replicate experiments or production pipelines.
• Ethical compliance Tracking fairness, privacy, and bias mitigation.

AI Content Metadata - Information about AI-generated content:

• Provenance markers indicating AI creation
• Generation parameters and prompts used
• Quality assessments and human review status
• Licensing and usage restrictions