Executive Briefing: Responsible AI and ML at scale across 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
As a Fortune 100 company operating in a highly regulated environment, Capital One’s ability to innovate with machine learning and AI can only move as fast as we are able to bring new frameworks for explainability to bare for systems operating in real-time and at scale. To this end, we are building an Explainable AI program, with the goal of embedding explainability and fairness into all of our ML platforms. In this paper, I will review our approach to explainable AI at Capital One, with particular focus on fairness in automated decisioning. I will share our key learnings on best practices in implementing fair and responsible AI systems, as well as the challenges we have faced along the way and the research efforts we’ve initiated to overcome them. I will review our approach to working with multidisciplinary experts from different fields to establish cross-functional working groups internally and in partnership with the academic community to ensure we are advancing the responsible use of AI/ML in a way that prioritizes ethical, interpretable decisions and fair treatment of people more broadly.
Prerequisite knowledgegeneral knowledge of ai/ml
What you'll learnUnderstanding the importance of a Responsible AI program and how to go about developing it in the enterprise.
Dr. David (Dave) Castillo leads Capital One’s Center for Machine Learning and Emerging Technology. In this role, Dave is responsible for driving excellence in Applied ML Research, University ML Research, ML technologies (tools and platforms), ML Consulting, and ML awareness within Capital One. Dave is a strong advocate of Responsible AI and has a keen interest in Automated Machine Learning and Timeseries ML.
For more than 25 years, Dr. Castillo has been involved with developing applications involving big data, artificial intelligence, machine learning, and large-scale distributed computing across a wide variety of industries. He has spent a great deal of time in Real-time ML for bidding on real-time auctions and delivering personalized advertising to online and mobile devices. He is a promoter of analyzing data streams “in flight” to extract meaningful content and for creating and delivering model features in near real-time. David is also experienced in developing and deploying fully automated self-learning models.
Dr. Castillo began his career developing artificial intelligence applications for NASA. He has since held positions as Chief Software Engineer for Motorola’s Iridium system, Chief Information Officer at KAST (an AI company), Chief Analytics Architect for Adenyo/Motricity, Chief Technology Officer at Voltari, and Chief Data Scientist for Early Warning Services. He has founded two startups in the areas of automated machine learning for online and mobile marketing and advertising.
Dr. Castillo holds a Bachelor’s in Engineering from the University of Arizona, a Master’s in Engineering from Arizona State University and earned a doctorate in Engineering from the University of Central Florida. He is an active speaker and participant in industry events and an Adjunct Professor of Computer Science at the University of Maryland University College.
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