Episode 44 — Understand Explainability Options: When You Need It and What Works (Domain 3)
Explainability is the degree to which a human can understand the cause of a decision made by an AI system, a critical requirement for high-stakes environments in Domain 3. This episode distinguishes between "black box" models like deep neural networks and "white box" models like decision trees, explaining the trade-offs between complexity and transparency. For the AAIR certification, you must understand when explainability is legally or operationally required, such as in loan denials or medical assessments. We explore various techniques like LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations) which provide insights into which features most influenced a specific model output. Troubleshooting explainability involves identifying when an "explanation" is actually a post-hoc rationalization that doesn't truly reflect the model's internal logic. By choosing the right explainability options, risk professionals ensure that AI systems are not only accurate but also justifiable to regulators, customers, and internal stakeholders, thereby fostering greater accountability and trust in automated decisions. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your educational path. Also, if you want to stay up to date with the latest news, visit DailyCyber.News for a newsletter you can use, and a daily podcast you can commute with.
