Why can't AI explain its own decisions?

The answer

Explainable AI (XAI) addresses the 'black box' problem: modern deep learning models with billions of parameters make highly accurate predictions but offer little insight into their reasoning. This is critical in high-stakes domains like healthcare, criminal justice, and finance where understanding the 'why' is as important as the 'what.' Techniques like SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model-agnostic Explanations), and attention visualization attempt to make model decisions interpretable. The EU AI Act requires that high-risk AI systems provide explanations for their outputs. However, there's a fundamental tension: the most explainable models (like decision trees) are often less accurate, while the most accurate models (like large transformers) remain the hardest to interpret.