Driving Regulatory Compliance: Key Pillars of AI Governance
Course Description
Identify key components of AI governance: model validation, explainability, audit trails, and regulatory compliance. This session will explore how institutions can embed compliance and governance into AI systems by focusing on model validation, explainability, auditability, and transparency.
Agenda
AI governance: What regulators expect.
The four pillars: Explainability, validation, traceability, and audit readiness
Risk-based documentation and testing protocols.
Common pitfalls in AI governance and how to avoid them.
Instructor: Stephanie Surratt
Course Description
Identify key components of AI governance: model validation, explainability, audit trails, and regulatory compliance. This session will explore how institutions can embed compliance and governance into AI systems by focusing on model validation, explainability, auditability, and transparency.
Agenda
AI governance: What regulators expect.
The four pillars: Explainability, validation, traceability, and audit readiness
Risk-based documentation and testing protocols.
Common pitfalls in AI governance and how to avoid them.
Instructor: Stephanie Surratt
Course Description
Identify key components of AI governance: model validation, explainability, audit trails, and regulatory compliance. This session will explore how institutions can embed compliance and governance into AI systems by focusing on model validation, explainability, auditability, and transparency.
Agenda
AI governance: What regulators expect.
The four pillars: Explainability, validation, traceability, and audit readiness
Risk-based documentation and testing protocols.
Common pitfalls in AI governance and how to avoid them.
Instructor: Stephanie Surratt