Understanding how to govern AI development
~27% of the exam · 3 competencies
Governance through the build: design decisions and risk management, data used for training and testing, and the controls around release, monitoring and maintenance.
Framing the business context and use case, assessing impact, applying policy and ethics to design choices, and identifying and managing risk before anything ships.
Lessons are coming as they’re published.
Data governance for model building: lawful rights to data, quality and fitness for purpose, lineage and provenance, and the testing regimes that prove the model behaves.
Lessons are coming as they’re published.
Readiness and release decisions, continuous monitoring, periodic assessment, incident management, root-cause analysis and public transparency obligations after release.
Lessons are coming as they’re published.