Popular Techniques for Explainable AI

Intermediate

🔍 Techniques for AI Interpretability & Explanation

Several techniques support interpretability and help generate explanations for AI model behavior:

📊 Feature Importance

Measures how much each feature influences the model's output.

  • 🛠️ Tools: SHAP, Permutation Importance

🧩 Local Explanations

Focus on individual predictions to explain why a specific decision was made.

  • 🔧 Tool: LIME (Local Interpretable Model-agnostic Explanations)

🌐 Global Explanations

Provide an overall understanding of the model’s behavior.

  • 🧱 Example: Extracting decision rules from complex models

🖼️ Visualization Tools

Visual aids enhance interpretability for both experts and non-experts.

  • 🔥 Heatmaps for CNNs (e.g., Grad-CAM)
  • 📈 Partial dependence plots
  • 🌳 Decision tree visualizations

🧮 Surrogate Models

Use simpler models to approximate complex models for explanation purposes.

  • 🧠 Example: Train a decision tree to mimic a neural network

✅ Practical Benefits

Implementing these techniques allows practitioners to:

  • 🔍 Interpret decisions
  • 📏 Validate outputs
  • 🙋 Trust AI models effectively