What is AI Explainability?
AI explainability (or interpretability) refers to the extent to which humans can understand and trace how an AI system reaches its decisions or predictions. It's crucial for building trust, ensuring accountability, meeting regulatory requirements, and debugging model behavior.
Why Explainability Matters
Trust Users need to understand why AI makes decisions.
Accountability Organizations must justify AI-driven outcomes.
Compliance Regulations require explainable decisions.
Debugging Understanding enables improvement.
Explainability Techniques
Model-Agnostic Methods
- LIME (Local Interpretable Model-agnostic Explanations)
- SHAP (SHapley Additive exPlanations)
- Feature importance analysis
- Partial dependence plots
Inherently Interpretable Models
- Decision trees
- Linear regression
- Rule-based systems
For Deep Learning
- Attention visualization
- Saliency maps
- Concept activation vectors
Explainability vs. Interpretability
- Interpretability: Understanding the model's mechanics
- Explainability: Communicating decisions to stakeholders
Both are essential for responsible AI deployment.