The Necessity of Explainable Artificial Intelligence: A Problem-Driven Perspective

Abstract

As artificial intelligence systems are increasingly used in important areas such as healthcare, finance, and security, the need for transparency in their decision-making has become critical. Many modern AI models function as black boxes, producing accurate results but offering no insight into how those results were reached. This lack of clarity can create serious risks, including reduced trust, unfair outcomes, and difficulties in identifying errors. Explainable AI (XAI) addresses these concerns by making model behaviour understandable to humans. This paper explains why explainability is essential, focusing on real-world risks, accountability requirements, and the need for safe and reliable AI systems. Instead of reviewing specific methods, the paper presents a problem-driven perspective that highlights explainability as a foundational requirement for responsible AI adoption.

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