Executive Briefing: Lessons from the front lines—Building a responsible AI/ML program in the enterprise
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.
What you'll learn
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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