Artificial Intelligence (AI) is increasingly shaping decisions in healthcare, finance, transport, and public services. Yet, many AI systems remain “black boxes,” leaving users frustrated and uncertain about outcomes. This course introduces Explainable AI (XAI), a framework for making AI decisions transparent, interpretable, and trustworthy. You will learn why explanations matter, the ethical and legal implications of opaque AI, and how XAI bridges the gap between highly accurate models and human understanding. For illustrative purposes and to bring the lessons to life, this course is replete with descriptive images—some of which are AI-generated. Your understanding will be enhanced when you mentally connect these illustrations to the lessons you have been taught. By mastering XAI, you can ensure AI systems are fair, accountable, and actionable.

You will explore both intrinsic and post-hoc explainability methods, including decision trees, linear models, SHAP, LIME, and counterfactual explanations. The course covers global and local interpretability, model-specific techniques like attention mechanisms and saliency maps, and model-agnostic approaches for understanding complex AI predictions. Through practical examples in credit scoring, medical diagnosis, and autonomous driving, you will see how these techniques reveal the reasoning behind AI decisions. Exercises will help you evaluate which method best suits different applications and stakeholders. Illustrative images further support your comprehension by visually connecting abstract concepts to practical examples.

Finally, you will learn to integrate XAI into real-world AI systems, considering human factors, regulatory requirements, and performance-interpretability trade-offs. Case studies highlight how explainability prevents bias in healthcare, supports fair lending in finance, enhances safety in transport, and ensures accountability in public services. You will gain hands-on experience with tools such as SHAP and LIME, draft actionable explanations for different user groups, and implement XAI as a design principle rather than a post-deployment feature. The inclusion of descriptive, AI-generated images reinforces the lessons, helping you mentally visualise complex AI processes and their practical applications. Take the first step toward mastering AI transparency and trust—enrol now and transform how you understand and apply AI in real-world systems.