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.
Practical example
A reviewer needs to know why a claim was rejected. Supply the actual rule, source reference, and application decision record.
What to check
Check whether an explanation matches observable evidence and is useful to the intended reviewer.
Limits and tradeoffs
A model-generated rationale may be plausible without describing the mechanism that produced the decision.
Maintained by DriftRail. AI assisted the October 2026 content and template revision. Examples are illustrative; they are not measured product benchmarks. Suggest a correction.