Interpretability - Now What?
Explore the frontiers of deep learning in this 48-minute talk by Been Kim from Google Brain, focusing on interpretability in machine learning. Delve into the goals and non-goals of interpretability, and learn about benchmarking interpretability methods (BIM) using three metrics for measuring false positives. Discover the Model Contrast Score (MCS) and the concept of Concept Activation Vectors (CAV). Examine the TCAV (Testing with Concept Activation Vectors) approach, including its core ideas, quantitative validation, and applications in image prediction models and medical diagnosis. Gain insights from human subject experiments comparing saliency maps, and understand the limitations and considerations of TCAV. Reflect on the broader implications and challenges in the field of interpretable machine learning.