Model Interpretability: Understanding AI Decisions
Understand how AI makes decisions: attention visualization, feature importance, LIME, SHAP, and interpretability techniques.
TL;DR
Interpretability techniques explain AI decisions: attention visualization (what model focuses on), feature importance (what matters), LIME/SHAP (local explanations), and probing (what model learned).
Why interpretability matters
- Debug model failures
- Build trust
- Regulatory compliance
- Detect bias
- Improve model
Techniques
Attention visualization: See which tokens model focuses on
Feature importance: Which inputs most influence output
LIME: Explain individual predictions with local approximation
SHAP: Game-theoretic feature attribution
Probing classifiers: Test what model learned
For language models
- Attention maps
- Token influence scores
- Layer-wise analysis
- Logit lens (predict at each layer)
Challenges
- Large models are complex
- Post-hoc explanations may be misleading
- Trade-off between accuracy and interpretability
Tools
- Transformers Interpret
- Captum (PyTorch)
- SHAP library
- BertViz (attention visualization)
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Key Terms Used in This Guide
Model
The trained AI system that contains all the patterns it learned from data. Think of it as the 'brain' that makes predictions or decisions.
AI (Artificial Intelligence)
Making machines perform tasks that typically require human intelligenceālike understanding language, recognizing patterns, or making decisions.
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