AI policy questions worth asking
A plain-English checklist for understanding AI safety, accountability and governance.
Who is responsible?
Ask who operates the system, who uses its output, and who is accountable when something goes wrong. A person affected by an automated decision should have a path to ask questions and request correction when appropriate.
What information is used?
Look for policies describing what data is collected, where it is sent, how long it is stored, and whether it is used to train systems. Check whether users can limit collection or request deletion.
How are errors and bias checked?
Good evaluations use realistic tasks, consider who might be disadvantaged by failures, and report limitations. Marketing claims alone do not establish safety or fairness.
Questions for any organization
- Can we explain the system’s purpose to users?
- Do we have a human escalation route?
- Can we audit important outputs and corrections?
- Do contracts and policies reflect the actual workflow?
- Are we reviewing applicable laws and standards before launch?
Requirements vary by location and use case; this guide is educational, not legal advice.
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