XAI techniques help interpret AI decisions. Some common methods include:

🔹 Feature Importance – Identifies which factors (or “features”) influenced the AI’s decision the most.
🔹 Decision Trees – A step-by-step flowchart that explains AI decisions in a structured way.
🔹 Local Interpretable Model-agnostic Explanations (LIME) – Simplifies complex AI models by creating easy-to-understand approximations.
🔹 SHAP (SHapley Additive exPlanations) – Breaks down how each input factor contributed to the AI’s decision.

Uses:

XAI is used in many critical fields:

🩺 Healthcare – AI explaining why it predicts a disease in medical scans.
🏦 Banking & Finance – AI explaining why a loan was approved or rejected.
🚔 Law Enforcement – AI ensuring fairness in criminal justice systems.
🚗 Self-Driving Cars – AI explaining why it took a particular action (e.g., stopping suddenly).