The Core Challenge
AI systems make consequential decisions affecting people's lives, but too often lack transparency, human oversight, or clear accountability when things go wrong.
Key Concepts
| Transparency | The ability to explain what an AI system does and why it made specific decisions. |
| Human oversight | Preserving human judgment at critical decision points, particularly when AI recommendations affect individuals' interests. |
| Accountability | Clear ownership and responsibility for AI system outcomes, with traceable decision-making. |
| Contestability | The right of affected individuals to understand and challenge AI-driven decisions. |
Warning Signs
Watch for these indicators that governance is inadequate:
- No one can clearly explain how the AI system makes decisions
- There's no documented owner accountable for the system's outcomes
- High-stakes decisions are made without human review
- Affected individuals have no way to challenge decisions
- When something goes wrong, it's unclear who knew what and when
- Impact assessments weren't done before deployment, or were superficial
Questions to Ask in AI Project Reviews
- "Walk me through what happens when this system makes a mistake. Who finds out, how, and what do they do?"
- "What human oversight exists at critical decision points?"
- "If an affected individual asked to understand why a decision was made about them, could we explain it?"
Questions to Ask in Governance Discussions
- "Who is the accountable owner for this AI system? What does that accountability actually mean?"
- "What impact assessment was done before deployment? What did it find?"
- "What audit trail exists to enable investigation if something goes wrong?"
Questions to Ask in Strategy Sessions
- "Do we have an AI register documenting systems in use, their purposes, and their governance status?"
- "How does our approach to AI accountability compare to regulatory expectations?"
- "What governance debt are we accumulating, and what's the plan to address it?"
Reflection Prompts
For your personal development, consider:
- In your area of responsibility: What AI systems affect people? Who is accountable for their outcomes?
- Your confidence level: If an AI system you're responsible for caused significant harm, could you demonstrate appropriate governance was in place?
- Your capability gap: What would you need to learn to more effectively govern AI accountability in your context?
Good Practice Checklist
You're on the right track when:
- AI systems have documented, accountable owners
- Impact assessments are conducted before high-stakes deployments
- Human oversight is preserved at critical decision points
- Affected individuals can understand and challenge decisions
- Audit trails enable investigation and accountability
- Governance is proportionate to risk
Quick Reference
| Element | Question to Ask | Red Flag |
|---|---|---|
| Ownership | Who is accountable? | "The AI team" / no clear individual |
| Transparency | Can we explain decisions? | "It's a black box" |
| Oversight | What human review exists? | Fully automated high-stakes decisions |
| Contestability | Can individuals challenge? | No mechanism exists |
| Audit | Can we trace what happened? | No logging or documentation |