AI Governance Foundations
AI governance begins with the system purpose, decision boundary and affected people, followed by clear human responsibility. Probabilistic output requires measurement of error across actual use groups. The team records
Read the whole first subject without an account: its lessons, summary and review questions.
Subject lessons
- Lesson 1 The learner distinguishes probabilistic AI output from deterministic rules and chooses a control proportionate to harm and context
- Lesson 2 The learner assigns governance decisions to an accountable authority and designs cross-functional collaboration and escalation when performance and harm indicators conflict
- Lesson 3 The learner turns fairness, safety, transparency and accountability principles into a measurement card with a threshold, owner and response to breach
- Lesson 4 The learner designs policy gates across the system lifecycle and ties change and incidents to review and stop authority
- Lesson 5 The learner builds AI supplier procurement and monitoring terms and designs awareness that tests user action when errors or purpose changes occur
- Lesson 6 The learner builds an independent assurance process for governance decisions, distinguishing control ownership from testing and routing challenge to a corrective authority
- Integrated subject summary Review questions
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