Explainability refers to making AI outputs understandable to humans, a necessity for trust, compliance, and accountability. This episode explains why explainability is distinct from accuracy: a model may perform well statistically yet still fail if users cannot understand its reasoning. The discussion highlights regulatory drivers such as rights to explanation in data protection laws, ethical imperatives around transparency, and practical needs for debugging and bias detection. Without explainability, AI systems risk rejection by regulators, organizations, and the public.

The episode explores examples across domains. Healthcare requires interpretable models to support clinician trust in diagnostic tools, while finance demands clear explanations of credit decisions to meet regulatory requirements. Generative models present new challenges where plausible but false outputs require users to understand limitations. Learners are also introduced to the concept of tailoring explanations to audiences, from technical staff to end-users. By the end, the importance of explainability as a safeguard for fairness, accountability, and adoption is clear. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your certification path.