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
What-if-tool → https://goo.gle/3ehZWbZ

Catch more ML Tech Talks → http://goo.gle/ml-tech-talks
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product: TensorFlow - General; re_ty: Publish;