Transparent documentation practices
Documenting AI systems properly helps build trust with users, stakeholders, and regulatory bodies. Good documentation practices make AI more accountable while setting appropriate expectations about what systems can and cannot do.
- Model cards for clear communication: Model cards are simple, standardized documents that explain AI systems to non-technical people. They describe what the AI does, how it was built, and where it might make mistakes. For example, a model card for a recommendation system would explain what data trained it, what types of items it recommends well, and where it struggles. Google, Microsoft, and other major AI developers have adopted this practice to increase transparency.[1]
- Data documentation approaches: Organizations should clearly document what information was used to build AI systems. This includes explaining data sources, collection methods, and known limitations. For instance, a speech recognition system trained primarily on American English speakers should document this potential bias toward certain accents. This transparency helps identify issues before they affect users.
- Version tracking for AI evolution: Teams should maintain clear records of how AI behavior changes over time. This includes documenting what changed between versions, why changes were made, and how performance metrics shifted. This creates accountability for system evolution and helps explain behavior changes to users who might notice differences.
