Explainable Artificial Intelligence (XAI) refers to a set of methods and techniques designed to make the outcomes of AI solutions comprehensible to human experts. By focusing on transparency and interpretability, XAI contrasts with traditional ‘black box’ models, where the rationale behind decisions remains unclear. This clarity is crucial in high-stakes fields such as healthcare, finance, and law, where understanding the reasoning behind AI-driven decisions is essential for trust, accountability, and ethical compliance. By enabling users to grasp how AI systems arrive at conclusions, XAI fosters informed decision-making and supports regulatory requirements, ultimately enhancing the reliability of AI technologies.
