Work in this lesson turns AI System Inventory, Impact Assessment, Threats, Bias, Privacy, and Security into a repeatable, reviewable practice. 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 impact and risk assessment, and tests the condition where training data introduces unmeasured bias into customer outcomes.
Complete a controlled exercise covering AI System Inventory, Impact Assessment, Threats, Bias, Privacy, and Security. Confirm scope and responsibilities, examine the stated exception, reach a supported conclusion, and preserve the outcome in AI impact and risk assessment.
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 “training data introduces unmeasured bias into customer outcomes” as a hypothesis until tested against the applicable criteria and intended outcome.
Learners can apply AI System Inventory, Impact Assessment, Threats, Bias, Privacy, and Security, explain the reasoning used, and defend the result through AI impact and risk assessment.