Episode 83 — Improve explainability so decisions are defensible to leaders and auditors (Task 20)
This episode explains how to improve explainability so AI-driven decisions are defensible to leaders and auditors, which AAISM tests through scenarios that require clear rationale, limits, and evidence rather than vague claims of “the model decided.” You will learn what explainability means in practical terms, including describing inputs, constraints, confidence signals, decision boundaries, and human oversight steps, and how to document these elements so stakeholders understand risk and accountability. We use examples like credit-like decisions, prioritization recommendations, or automated approvals to show how to communicate what the model can and cannot reliably do, and where human judgment remains required. Troubleshooting focuses on overpromising certainty, relying on explanations that are not stable across versions, and failing to connect explainability to monitoring and change control that keeps claims accurate over time. 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.
