AI Glossary
Model Documentation
Model Documentation is the collection of records and information that describe how an AI model was developed, tested, deployed, and monitored.
Model Documentation
Overview
Imagine buying a complex machine without receiving instructions, specifications, maintenance records, or safety information.
Understanding how that machine works would be difficult.
AI systems face a similar challenge.
Without documentation, organizations may struggle to understand how models were built, what data was used, what limitations exist, or how decisions were made.
Model Documentation refers to the records and information that describe an AI model throughout its lifecycle.
Documentation helps create transparency and accountability.
It provides important context for developers, auditors, governance teams, compliance professionals, and business stakeholders.
Model documentation may include:
- Model purpose
- Training data information
- Performance metrics
- Testing results
- Known limitations
- Risk assessments
- Monitoring procedures
- Version history
Good documentation helps organizations answer important questions.
How was the model trained?
What assumptions were made?
What risks were identified?
How is performance monitored?
Without documentation, these answers may be difficult to obtain.
As governance and compliance requirements continue evolving, documentation is becoming a critical component of responsible AI programs.
Many organizations now require documentation before AI systems can be approved for deployment.
Why It Matters
Documentation improves transparency, supports audits, strengthens governance efforts, and helps organizations manage AI systems more effectively.
Real-World Example
Before deploying a predictive analytics model, a company creates a record describing training data sources, testing results, limitations, and monitoring procedures.
This record is model documentation.