Many powerful AI models — especially deep neural networks — are black boxes: they produce outputs without explaining why. This creates serious problems when decisions affect people's lives.

Why Explainability Matters

• Individuals denied a loan or job deserve to know why
• Doctors need to understand AI diagnostic suggestions
• Regulators need to audit systems for compliance
• Developers need to debug and improve models

Explainability Methods

• LIME: Explains individual predictions locally
• SHAP: Shows which features drove each prediction
• Attention visualization: Shows what the model "focused on"

Reference:

Interpretable Machine Learning book

https://christophm.github.io/interpretable-ml-book/