Sep 23–26, 2019

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

Michael Kubiske (Captial One)
1:15pm1:55pm Thursday, September 26, 2019
Location: 1E 10/11
Secondary topics:  Culture and Organization, Ethics

Who is this presentation for?

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




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.

Prerequisite knowledge

16:9 screen for a standard powerpoint presentation. will also need microphone, confidence monitor, and remote clicker.

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 Michael Kubiske

Michael Kubiske

Captial One

Michael Kubiske has a wide background in model development, production deployments, economic research, and project management. He joined Capital One as a Director of Machine Learning Engineering in the Center for Machine Learning (C4ML) in 2017 and is now the Chief Machine Learning Engineer / Architect in C4ML. In his spare time, he researches non-standard indicator signals to predict and automatically trade stocks on his home cluster. He is an avid cyclist living with his wife and two daughters in NYC.

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