What Is Explainability (XAI)?

Deep learning models are often 'black boxes' — they produce accurate results but cannot easily explain their reasoning. Explainability addresses this by providing insights into model behavior. Techniques include feature attribution (which inputs mattered most), attention visualization (what the model focused on), counterfactual explanations (what would need to change for a different output), and model distillation into interpretable forms. Explainability is crucial in high-stakes domains like healthcare (doctors need to understand AI recommendations), finance (regulations require explanation of credit decisions), and criminal justice (defendants have a right to understand decisions). The EU AI Act and other regulations increasingly mandate explainability for certain AI applications.