Episode 100 — Audit data quality before trusting any AI output or model score (Domain 3D)
This episode teaches you why auditing data quality must happen before you trust any AI output or model score, because Domain 3D scenarios often hinge on the fact that “good models” fail when inputs are wrong, incomplete, biased, or out of date. You’ll learn how to evaluate data quality dimensions that matter for audit conclusions—accuracy, completeness, consistency, timeliness, representativeness, and label reliability—and how each dimension maps to specific decision risks like unfair outcomes, unstable performance, and undetected drift. We’ll cover how to test data quality using pipeline validation logs, exception handling records, sampling of source data, and comparisons across segments that reveal representation gaps and uneven error patterns. You’ll also learn how quality controls should be evidenced over time, including monitoring thresholds, remediation workflows, and governance decisions when quality issues require limiting automation or revisiting requirements. 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.
