Glossary
What is AI Explainability?
Making AI decisions understandable to humans.
What is AI explainability?
AI explainability (XAI) refers to techniques that make AI decisions understandable to humans. For LLMs, this includes understanding why a model generated a particular response, what context it used, and how confident it is.
Explainability Techniques
- Chain-of-thought: Model explains reasoning steps
- Attention visualization: See what context model used
- Confidence scores: Model uncertainty estimates
- Citations: Sources for claims
Why It Matters
- Trust: Users can verify AI reasoning
- Debugging: Understand why outputs are wrong
- Compliance: Regulations require explanations
- Improvement: Identify model weaknesses
Regulatory Requirements
- EU AI Act requires explanations for high-risk AI
- GDPR right to explanation for automated decisions
- Industry-specific requirements (finance, healthcare)
Why does explainability matter?
Explainability enables trust, debugging, and compliance. Regulations like the EU AI Act require explanations for high-risk AI decisions. Users need to understand AI reasoning to trust and verify outputs.
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