XAI (eXplainable Artificial Intelligence) refers to methods and techniques that make AI system decisions understandable by humans, a crucial issue for trust and compliance.
What is XAI?
XAI (eXplainable Artificial Intelligence) encompasses all methods that make artificial intelligence system decisions understandable and interpretable by humans.
Why is XAI important?
- Trust: Understanding why an AI gives an answer
- Compliance: European AI Act transparency requirements
- Debugging: Identifying and correcting biases
- Improvement: Optimizing models by understanding how they work
XAI and AI visibility
Understanding how AI "thinks" helps optimize content:
- AI like ChatGPT increasingly cite their sources
- Perplexity explicitly displays used sources
- Claude can explain its reasoning
Common XAI techniques
- LIME: Local Interpretable Model-agnostic Explanations
- SHAP: Shapley values for feature importance
- Attention maps: Attention visualization in transformers
- Chain-of-thought: Step-by-step reasoning
XAI and AI Act
The European AI Act regulation imposes transparency requirements for high-risk AI systems, making XAI essential for compliance.