Explainable Artificial Intelligence

Explainable artificial intelligence studies how to show why models make particular predictions or take actions, so people can assess errors, bias, uncertainty, and appropriate use. Common approaches include post-hoc feature attribution such as SHAP and LIME, saliency maps for images, counterfactual examples, and models designed around interpretable rules or concepts; newer systems also turn evidence and execution traces into interactive or natural-language explanations. Researchers test whether explanations reflect model behavior and remain stable, but increasingly also measure whether people can understand them and make better decisions in settings such as healthcare, finance, cybersecurity, and autonomous systems. A central tension is that explanations that are faithful to complex models may be difficult to use, while fluent or persuasive explanations can encourage misplaced trust without improving understanding or decisions.