The Center for Democracy & Technology (CDT) has released a report titled "Improving Governance Outcomes Through AI Documentation: Bridging Theory and Practice." The report emphasizes the importance of AI documentation as a tool for governing AI systems, providing stakeholders both within and outside AI organizations with insights into the development, functionality, and risks associated with these systems.
"AI documentation is a foundational tool for governing AI systems," the report states. It highlights that effective documentation can assist internal teams in managing risk throughout the development and deployment lifecycle. Additionally, it can guide external technology developers on necessary testing procedures and help users decide whether to adopt specific technologies.
The report synthesizes findings from an analysis of academic and gray literature on documentation, covering 37 proposed methods for documenting AI data, models, systems, and processes. It also includes 21 empirical studies evaluating the impact and challenges of implementing such documentation. Key theoretical mechanisms identified include informing stakeholders about intended use, limitations, and risks; facilitating cross-functional collaboration; prompting ethical reflection among developers; and reinforcing best practices in development and governance.
However, the empirical evidence presents mixed support for these mechanisms. This indicates that current documentation practices may need to be more effectively designed to achieve these goals.
The report outlines several design trade-offs organizations must consider when developing documentation strategies. Customized documentation can address specific risks but may reduce comparability across artifacts. In contrast, standardized formats promote consistency but may overlook system-specific details. Organizations also face decisions about creating single or multiple tailored artifacts to serve diverse stakeholders better but at the cost of increased maintenance complexity.
Another critical consideration is determining the appropriate level of detail in documentation—too much information can overwhelm users while too little may omit essential details. The report also explores trade-offs involved in automating the documentation process versus developing interactive interfaces for thorough stakeholder exploration.
Recommendations for designing effective documentation processes include assessing organizational capacity realistically, identifying key stakeholder needs, prioritizing essential details, and regularly evaluating progress against success criteria.
By carefully designing and implementing tailored documentation processes that meet diverse stakeholder needs, organizations can establish a robust foundation for managing AI system risks. Regular assessment and refinement of these practices are crucial for contributing to improved AI governance over time.
For further details, readers are encouraged to access the full report provided by CDT.
