Explainable AI Workshop — A Unified Framework for Model Explanation

With Ian Covert from University of Washington

Explainable AI is becoming increasingly important, but the field is evolving rapidly and requires better organizing principles to remain manageable for researchers and practitioners.

In this talk, Ian will discuss a new paper that unifies a large portion of the literature using a simple idea: simulating feature removal. The new class of “removal-based explanations” describes 20+ existing methods (e.g., LIME, SHAP) and reveals underlying links with psychology, game theory and information theory.

Practical examples will be presented and available on the Qu.Academy site

Reference:
Explaining by Removing: A Unified Framework for Model Explanation
Ian Covert, Scott Lundberg, Su-In Lee

Explaining by Removing: A Unified Framework for Model Explanation

The full video of the discussion is here:

Podcast from the event

Slides, demos, and videos at

QuantUniversity

QuantUniversity partnered with PRMIA for QuantUniversity’s fall school in Machine Learning and AI in Finance. We had more than 1000 participants from more than 20 countries including India, China, Australia, UK, Turkey, South Africa etc. attend the our courses.

This fall, we are offering 3 courses in Data Science, Machine Learning, and Model Risk Management:

1. Just Enough Python for Data Science
2. Machine Learning and AI for Financial Professionals
3. Model Risk Management for Machine Learning Models

Join us for the upcoming QuantUniversity Winter School 2021