Episode 96 — Design sampling for AI decisions that reveals bias and failure modes (Domain 3B)
This episode focuses on designing sampling approaches that reveal bias and failure modes in AI decisions, because AAIA questions often ask what sampling plan best supports a defensible conclusion. You’ll learn how to sample across time, segments, and decision types so you can detect drift, representation gaps, and inconsistent outcomes that hide inside averages. We’ll cover how to choose samples that reflect decision impact, including oversampling edge cases, high-risk categories, and scenarios that historically produce complaints or manual overrides. You’ll also learn how to tie sampling to criteria, such as fairness thresholds, policy boundaries, and escalation requirements, so the sample proves whether controls operate as intended. Practical considerations will include ensuring your sample can be traced to logs, model versions, and data states, so results are reproducible and not disputed as “from a different model.” By the end, you should be able to choose exam answers that use sampling as a detection tool for real-world harm, not just as a box-checking method. 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.
