XAI (Explainable AI)

Executive Definition: XAI (Explainable AI) — A set of methods and techniques designed to make the decision-making processes of AI models transparent, understandable, and auditable by humans.

XAI bridges the gap between complex model predictions and human understanding. Using techniques like SHAP values or feature importance scores, XAI helps developers debug models, allows businesses to build trust with users, and ensures regulatory compliance in critical fields like finance and healthcare.

Frequently Asked Questions

Why does explainability matter?

Regulations like GDPR give individuals the right to an explanation of automated decisions. Beyond compliance, explainability builds trust with stakeholders and helps debug model errors.

Are LLMs explainable?

LLMs are inherently difficult to explain due to their complexity. Chain-of-thought prompting provides some transparency by showing the model’s reasoning steps, but true mechanistic interpretability is still an active research area.

What tools exist for XAI?

SHAP and LIME are popular for traditional ML models. For LLMs, attention visualization, chain-of-thought prompting, and model cards provide partial explainability.