Documentation of assumptions and limitations
Documentation of assumptions and limitations involves clearly recording the underlying beliefs, conditions, and boundaries that influence a project, analysis, or study. This process ensures transparency by highlighting what has been presumed true and recognizing any constraints or factors that may affect outcomes. Proper documentation helps stakeholders understand the context, assess the reliability of results, and identify areas where further research or caution may be necessary, ultimately supporting informed decision-making.
Documentation of assumptions and limitations
Documentation of assumptions and limitations involves clearly recording the underlying beliefs, conditions, and boundaries that influence a project, analysis, or study. This process ensures transparency by highlighting what has been presumed true and recognizing any constraints or factors that may affect outcomes. Proper documentation helps stakeholders understand the context, assess the reliability of results, and identify areas where further research or caution may be necessary, ultimately supporting informed decision-making.
💡 Key Takeaways
- Document core assumptions behind the AI model, data, and evaluation.
- Distinguish explicit from implicit assumptions and note their potential impact.
- Record limitations and constraints like data quality, scope, deployment context, and failure modes.
- Explain how transparent assumptions help governance, risk management, and accountability.
- Create a living process to review and update assumptions as models or contexts change.
❓ Frequently Asked Questions
What is documentation of assumptions and limitations in AI governance?
It is the practice of recording the beliefs, conditions, and boundaries that shape a project or model, making explicit what is assumed to be true and which constraints may affect outcomes.
What should be documented as an assumption?
Statements about expected data quality and availability, environmental conditions, input distributions, model training conditions, and external factors that are taken to be true for the project.
Why are limitations important to document?
To set realistic expectations, guide risk assessment, and inform stakeholders about where performance may be uncertain or conditions may invalidate results.
How does documentation support AI governance and control?
It enables monitoring and re-evaluation as conditions change, supports audits and accountability, and informs what controls or mitigations are needed.