Explainable AI (XAI) is tackling the problem of opaque decision-making processes in AI systems. With the increasing demand for transparency, XAI aims to lift the veil on machine learning algorithms, providing humans with insights into how AI makes decisions. Feature attribution techniques and interpretable models are two approaches being explored. XAI not only helps in understanding AI’s decision-making but also addresses legal and ethical implications, such as algorithmic bias and accountability. Collaboration across disciplines is crucial to refine and advance the field of XAI.