AI risk models are changing how organizations assess vendors, quantify cyber risk, and make high-stakes decisions, but most leaders don’t truly understand what’s happening under the hood. In this episode, AI risk models are stripped down and stress-tested to reveal where automation adds clarity, where it creates blind spots, and why overconfidence in models can quietly increase risk. If you’ve ever struggled to explain or defend an AI-driven decision, this conversation delivers the context you’ve been missing.

Hosted by Bob Maley, Jeffrey Wheatman, and Ferhat Dikbiyik, this episode of Third Party explores why all models are inherently flawed, how AI amplifies both insight and uncertainty, and why human judgment still matters in third-party risk management. The hosts unpack real-world examples from vendor scoring, cyber risk quantification, and executive decision-making to show why explainability, defensibility, and adaptability matter more than perfect accuracy.

Don’t risk trusting models you can’t justify. Learn how to use AI without surrendering accountability before the wrong decision gets made for you.