Quantitatively Assessing Explainable AI for Deep Neural Networks

Explore quantitative methods for evaluating explainable artificial intelligence techniques applied to deep neural networks in this comprehensive lecture delivered by Dr. Zullich as part of the AI Doctoral Academy's AICET2025 program, covering systematic approaches to measure and assess the effectiveness of XAI methods, evaluation metrics for interpretability, benchmarking frameworks for explainability techniques, and practical methodologies for quantifying the quality and reliability of explanations generated by deep learning models.