Interpretable Machine Learning focuses on making the decision-making processes of complex models transparent and understandable to humans, ensuring trust and accountability in AI applications. It is crucial for applications where human oversight is necessary, such as healthcare, finance, and autonomous systems, to ensure ethical and fair use of AI technologies.