Explainable AI (XAI) refers to methods, techniques, and tools that make AI system outputs understandable to human users. As AI models — particularly deep learning and large language models — grow more complex, their decision-making processes become opaque "black boxes," creating challenges for trust, accountability, and regulatory compliance.
XAI approaches fall into two categories: intrinsically interpretable models (simpler models like decision trees or linear regression that are inherently understandable) and post-hoc explanation methods (techniques applied to complex models to explain their decisions after the fact).
Common XAI techniques include: LIME (Local Interpretable Model-agnostic Explanations) — explaining individual predictions by approximating the model locally, SHAP (SHapley Additive exPlanations) — using game theory to attribute feature importance, attention visualization — showing which input elements the model focused on, counterfactual explanations — describing what input changes would change the outcome, concept-based explanations — relating model behavior to human-understandable concepts, and natural language explanations generated by the model itself.
Enterprise applications of XAI include: regulatory compliance (GDPR's right to explanation, EU AI Act transparency requirements), risk management (understanding why AI systems make certain decisions), bias detection (identifying which features drive potentially discriminatory outcomes), debugging and improvement (understanding model failures to improve performance), and stakeholder trust (building confidence in AI systems among users, customers, and regulators).
