This talk introduces the field of Explainable AI, outlines a taxonomy of ML interpretability methods, walks through an implementation deepdive of Integrated Gradients, and concludes with discussion on picking attribution baselines and future research directions.

Chapters:
00:00 Intro
2:31 What is Explainable AI?
8:40 Interpretable ML methods
14:52 Deepdive: Integrated Gradients (IG)
39:13 Picking baselines and future research directions

Resources:
Integrated gradients → https://goo.gle/2PxfRtq
Vertex AI → https://goo.gle/3ifu7S5
Whatiftool → https://goo.gle/3ehZWbZ

Catch more ML Tech Talks → http://goo.gle/mltechtalks
Subscribe to TensorFlow → https://goo.gle/TensorFlow


product: TensorFlow General; re_ty: Publish;