Explainable AI

This course provides a deep dive into the key concepts, techniques, and real-world applications of Explainable AI, equipping participants with the skills to develop and deploy interpretable and transparent AI systems.

This short course provides a comprehensive introduction to the field of Explainable Artificial Intelligence (XAI). Participants will learn the key concepts, techniques, and real-world applications of XAI, which aims to make AI systems more interpretable and transparent.The course covers topics such as the interpretability problem, feature importance, saliency maps, counterfactual explanations, and intelligible models. Learners will also explore XAI regulations, the "right to explanation", and strategies for communicating XAI insights to stakeholders.Through a combination of lectures, hands-on exercises, and case studies, participants will gain the skills to develop and deploy XAI solutions that promote trust, accountability, and responsible AI practices within their organizations.