What is XAI (Explainable AI)?
XAI (Explainable AI) — Methods and techniques that allow human users to understand and trust the results of machine learning algorithms.
XAI techniques make AI decisions transparent and auditable. Methods like SHAP values, attention visualization, and feature importance scores show why a model made a specific prediction. This is critical for regulated industries where decisions must be justified to auditors.
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.