What is explainable AI (XAI)?

Definition: Explainable AI (XAI) is the set of methods that make a model's outputs understandable to people, showing which inputs drove a prediction or decision so it can be trusted, debugged and audited.

Why explainability matters

When a model denies a claim or flags a transaction, reviewers, customers and regulators may need to know why. Explanations also help engineers find data leaks, spurious correlations and failure modes before they reach users.

Common techniques

Interpretable models such as linear models and shallow trees are explainable by design. For complex models, feature attribution methods like SHAP and LIME estimate each input's contribution, counterfactuals show what would have changed the result, and attention or saliency maps highlight influential tokens or pixels.

Explainability for LLMs

Large language models are harder to explain internally. Practical approaches include citing retrieved sources in RAG, logging the tools and steps an agent took, and recording prompts and outputs through AI observability. A model's self-written reasoning is useful context but is not a guaranteed account of how it reached an answer.

How Ryz helps

Ryz machine learning engineers add attribution, source citations and decision logs to models in production, working on your team.

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