The Governed AI Checklist: 12 Questions Every Institution Must Answer
September 1, 2024 • 8 min read • By Dr. Sarah Chen, Head of AI Governance
When implementing AI in regulated environments, the difference between success and costly failure often comes down to asking the right questions upfront. This checklist distills our experience working with government agencies, financial institutions, and critical infrastructure providers into 12 essential questions every institution must answer before deploying AI systems.
Data Governance & Privacy
1. Where will our data be processed and stored?
Why it matters: Data residency requirements vary by jurisdiction and sector. GDPR, financial regulations, and government security policies all impose specific constraints.
2. How will we ensure data minimization?
Why it matters: Collecting only necessary data reduces privacy risks and regulatory exposure while improving system performance.
3. What consent and legal basis do we have?
Why it matters: AI processing often requires explicit legal justification, especially when handling personal data or making automated decisions.
Operational Controls
4. Where do humans stay in the loop?
Why it matters: High-stakes decisions require human oversight. Defining these touchpoints prevents automated errors from causing significant harm.
5. How will we monitor system performance?
Why it matters: AI systems can degrade over time due to data drift, changing conditions, or adversarial inputs. Continuous monitoring is essential.
6. What happens when the system fails?
Why it matters: All systems fail eventually. Having clear procedures minimizes disruption and maintains service continuity.
Compliance & Auditability
7. How will we explain AI decisions?
Why it matters: Regulators, auditors, and affected parties often require explanations for automated decisions, especially in high-stakes contexts.
8. What audit trails do we need?
Why it matters: Comprehensive logging enables compliance verification, incident investigation, and continuous improvement.
9. How will we handle bias and fairness?
Why it matters: Biased AI systems can perpetuate discrimination and create legal liability, especially in public services and regulated industries.
Risk Management
10. What are our security requirements?
Why it matters: AI systems present unique attack vectors, from data poisoning to model extraction. Security must be built in from the start.
11. How will we manage third-party risks?
Why it matters: AI vendors, cloud providers, and data processors create additional risk exposure that must be carefully managed.
12. What's our incident response plan?
Why it matters: When AI systems cause harm or make errors, rapid response can minimize damage and demonstrate responsible governance.
Next Steps
This checklist provides a starting framework, but every institution's needs are unique. Consider conducting a governance readiness assessment to identify your specific requirements and priority areas.
The goal isn't perfection-it's building AI systems that deliver value while maintaining the trust and accountability your stakeholders expect.