What if the secret to successful AI governance lies in understanding the evolution of model documentation? In this episode, our hosts challenge the common belief that model cards marked the start of documentation in AI. We explore model documentation practices, from their crucial beginnings in fields like finance to their adaptation in Silicon Valley. Our discussion also highlights the important role of early modelers and statisticians in advocating for a complete approach that includes the entire model development lifecycle.

Show Notes

Model documentation origins and best practices (1:03)

• Documenting a model is a comprehensive process that requires giving users and auditors clear understanding: 
  • Why was the model built? 
  • What data goes into a model? 
  • How is the model implemented? 
  • What does the model output? 

Model cards - pros and cons (7:33)

• Model cards for model reporting, Association for Computing Machinery
• Evolution from this research to Google's definition to today
• How the market perceives them vs. what they are
• Why the analogy “nutrition labels for models” needs a closer look

System cards - pros and cons (12:03)

• To their credit, OpenAI system cards somewhat bridge the gap between proper model documentation and a model card.
• Contains complex descriptions of evaluation methodologies along with results; extra points for reporting red-teaming results
• Represents 3rd-party opinions of the social and ethical implications of the release of the model

Automating model documentation with generative AI (17:17)

• Finding the balance in automation in a great governance strategy
• Generative AI can provide an assist in editing and personal workflow

Improving documentation for AI governance (23:11)

• As model expert, engage from the beginning with writing the bulk of model documentation by hand.
• The exercise of documenting your models solidifies your understanding of the model's goals, values, and methods for the business

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