High-level journey
How the pieces connect
- 1Use case
Define the purpose, the benefit and who could be affected.
- 2Risk assessment
Assess impact, fairness, privacy and safety risks.
- 3Data
Training and input data inherit data governance controls.
- 4Build & test
Evaluate performance, bias and robustness before release.
- 5Human oversight
Define who can intervene, override or stop the system.
- 6Monitor
Track drift, incidents and outcomes after deployment.
Four lenses
Questions worth asking
Accountability
Who is responsible for the AI's outcomes?
Data
Is the data fit, lawful and representative?
Risk
What harms are possible, and how are they mitigated?
Transparency
Can we explain what it does and why?
Public Sources & Further Reading
External references for further learning. Content on this page is original educational interpretation.
- Government guidance · Australian GovernmentAustralia's AI Ethics PrinciplesTopic: Responsible AI principles · Last reviewed 2026-10-07
- Government guidance · Australian GovernmentVoluntary AI Safety StandardTopic: AI guardrails · Last reviewed 2026-10-07
- Public standard · NISTAI Risk Management FrameworkTopic: AI risk management · Last reviewed 2026-10-07
- Public standard · ISOISO/IEC 42001 AI management systemsTopic: AI management system standard · Last reviewed 2026-10-07
AI Governance is a developing focus. This educational overview is available now; interactive journeys are not yet available.