The lesson examines how AI Risk Governance, Context, Principles, Roles, Appetite, and Accountability influences responsibilities, controls, evidence, and outcomes. The activity uses a regulated organization deploying predictive models and generative AI for customer decisions and internal productivity and contributes to the wider objective to lead AI risk governance, assessment, treatment, monitoring, and communication across the AI lifecycle.
The instructor traces a case involving a regulated organization deploying predictive models and generative AI for customer decisions and internal productivity. The demonstration links decisions to objective evidence, records the result in AI risk governance charter, and tests the condition where business ownership is unclear for a high-impact model.
Complete a controlled exercise covering AI Risk Governance, Context, Principles, Roles, Appetite, and Accountability. Confirm scope and responsibilities, examine the stated exception, reach a supported conclusion, and preserve the outcome in AI risk governance charter.
Start where expected practice and observed evidence first diverge. Separate a missing record from an ineffective process, distinguish unclear ownership from a control failure, and treat “business ownership is unclear for a high-impact model” as a hypothesis until tested against the applicable criteria and intended outcome.
Learners can apply AI Risk Governance, Context, Principles, Roles, Appetite, and Accountability, explain the reasoning used, and defend the result through AI risk governance charter.