Explainable Machine Learning (XAI)

As Artificial Intelligence (AI) becomes integrated into high-risk domains like healthcare, finance, and criminal justice, it is critical that those responsible for building these systems think outside the black box and develop systems that are not only accurate, but also transparent and trustworthy. This course is a comprehensive, hands-on guide to Explainable Machine Learning (XAI), empowering you to develop AI solutions that are aligned with responsible AI principles. Through discussions, case

• Explain and implement model-agnostic explainability methods.
• Visualize and explain neural network models using SOTA techniques.
• Describe emerging approaches to explainability in large language models (LLMs) and generative computer vision.