AI risk governance
summary
Argues that existing model-risk frameworks scale to AI by adding concrete practices: require independent review plus enhanced monitoring for production models, use integrated platforms that keep code data hyperparameters and version control together, and produce model cards, audit trails and reproducibility checks. Recommends a risk-based lifecycle—concept, build, validate, monitor, retire—with incremental due diligence, tighter change management for retraining, and an enterprise community of practice to govern high-impact cases like automated loan decisions.
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chapters (9)
- 0:00Intro
- 2:04AI definition
- 4:02AI and trust
- 5:33AI risks
- 7:40Model risk
- 15:42Principles
- 17:15Learnings
- 22:51Proactive Agile Collaborative
- 26:36Things to take away
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