Explainable Artificial Intelligence (XAI): Interpreting Black-Box Models

Artificial intelligence has quickly moved beyond research laboratories and into everyday decision-making in areas such as healthcare, finance, employment, and public safety. While these systems often achieve impressive accuracy, many operate as “black boxes,” offering decisions without clear explanations that people can understand or challenge. This paper explores why such opacity undermines trust, accountability, and fairness, and presents Explainable Artificial Intelligence (XAI) as a practical response to this problem. It introduces key ideas behind XAI, including the difference between local and global explanations, and reviews widely used methods such as LIME and SHAP. Through real-world examples from criminal justice, public administration, and healthcare, the study shows how explanations can support ethical decision-making, legal responsibility, and effective human-AI collaboration. At the same time, it highlights the limits of current XAI approaches, emphasizing the need for careful use, critical evaluation, and stronger regulatory oversight.