Explore the principles of Explainable AI in this 32-minute lecture from the University of Central Florida's CAP6412 course. Delve into the challenges of interpreting deep learning models, examining alternative explanation techniques beyond Taylor Decomposition. Learn about the four key properties of effective explanation methods and understand the Layer-wise Relevance Propagation (LRP) rules for deep rectifier networks. Discover how to implement LRP efficiently and its connection to Deep Taylor Decomposition. Analyze the properties of explanations and explore rule choices using the VGG-16 network. Gain valuable insights into the importance of explainability in AI and its implications for various applications.