Sep 23–26, 2019

Executive Briefing: Lessons from the front lines—Building a responsible AI/ML program in the enterprise

Keegan Hines (Capital One)
1:15pm1:55pm Thursday, September 26, 2019
Location: 1E 10/11
Secondary topics:  Culture and Organization, Ethics
Average rating: ****.
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Who is this presentation for?

This talk will be appropriate for both technical (engineers, data scientists, architects, researchers) and non-technical audience members

Level

Beginner

Description

Explainability is so hot right now. Definitions for explainability are very diverse per field, per model, per use case, per researcher, etc… So what does explainability mean to a bank turned tech company like Capital One? More importantly, what does it mean in general? Uniquely among many business types, our explainability frontier is in responsibly pushing boundaries on new modeling types and frameworks while maintaining a level of rigor expected from our regulators.

This talk will explore some of the philosophy around the concept of explaining a model given the colloquial definition is partially recursive. It will cover the lens banking regulation places on this philosophical basis and expand into techniques used for these well governed aspects. In addition, we will cover some of the important thought leaders and highlight some research in explanatory fields of research in the DS/ML/AI space. Lastly we will explore potential impetus and example corporate structures in application of these philosophies.

What you'll learn

A general understanding of AI/ML and the broader societal considerations that are being discussed in the space with regard to fairness, ethics, transparency, and responsibility.
Photo of Keegan Hines

Keegan Hines

Capital One

Keegan Hines is Director of Machine Learning Research at Capital One where he leads development in areas including Explainable AI, Representation Learning, ML on Graphs, and Computer Vision. He is also an adjunct professor at Georgetown University, teaching graduate coursework in statistics and machine learning.

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