Explore governance of foundation models and generative AI products. Learn about evaluating and governing these technologies in third-party management and model risk contexts.
Learn how to scale model documentation methods for AI and complex modeling systems. Avoid the common mistakes that teams make early on.
In the aftermath of a worldwide IT incident, what can we learn about how to properly build and govern robust, performant, resilient AI systems, the right way.
What should you consider when teams tell you they’ve got AI “covered”? To increase your chances of success with AI, align your organization and projects around the following actions.
Learn about the goals of information theory, define the differences between metrics and divergences, explain why divergences are the wrong choice for monitoring, and propose better alternatives.
Many of the AI-based innovations used by enterprises are from specialty technology vendors. Get answers from a general counsel about why formal governance is critical to everyone's success with AI.
When is "easy" too good to be true? Learn more about the fine line between automating business operations and automating their governance.
Here is the supplement to our episode about non-parametric statistics. Learn from sample tests using Python 3.9 and popular scientific computing libraries.
The outcomes of the AI safety debate will directly affect your organization and your corporate responsibilities. Few enterprises have established a dedicated AI governance function, but with regulation taking shape and the business impact growing, how this function will be structured, tasked, and resourced are near-term questions.
The success of AI systems – effectiveness, safety, return on investment – depends on the right people coming together from across the business. Discover the roles that make this happen.
