As AI models become more complex, they increasingly act as 'black boxes'. Explainable AI (XAI) is critical for regulated industries (healthcare, finance) to ensure decisions are transparent, fair, and debuggable.
Module 1: Mechanistic Interpretability
Mechanistic Interpretability attempts to reverse-engineer neural networks, identifying specific neurons or circuits responsible for distinct concepts (like identifying a 'Golden Retriever' feature inside a CNN layer, or a 'sarcasm' attention head in a Transformer).
Common XAI Methods
- SHAP (SHapley Additive exPlanations): Game theory approach to assign credit to input features.
- LIME: Local interpretable model-agnostic explanations.
- Attention Visualization: Mapping attention weights in transformers.