AI systems are powerful, but when their outputs cannot be understood, they risk losing trust. This episode explores transparency and explainability as core qualities for responsible AI. We begin by distinguishing between transparency — openness about how systems are designed and trained — and explainability, which focuses on how specific decisions or predictions are made. White-box models like decision trees and linear regression are contrasted with black-box systems like deep neural networks, which achieve high accuracy but resist easy interpretation. Post-hoc techniques such as LIME and SHAP are introduced as tools for interpreting complex models, while documentation practices like model cards and datasheets add accountability.
We also consider why explainability matters in practice. In healthcare, clinicians need to understand AI recommendations for patient safety. In finance, lending models must be explainable to comply with laws that protect consumers from discrimination. In government, algorithmic decisions that affect rights and opportunities must be transparent to uphold democratic accountability. Challenges include balancing interpretability with performance, ensuring explanations are meaningful to non-technical users, and avoiding superficial “explanations” that obscure deeper problems. By the end, listeners will understand that transparency and explainability are not optional extras — they are prerequisites for building AI systems that are trustworthy, auditable, and aligned with human values. Produced by BareMetalCyber.com, where you’ll find more cyber prepcasts, books, and information to strengthen your certification path.
