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
I will discuss how Capital One, one of the largest banks in the U.S., is building a Responsible AI program, and offer best practices and insights for other organizations to consider. Within this context, I will expound on the potential risks and challenges that, if not well-managed and done responsibility, an AI and ML program can present for any organization. As a Fortune 100 company operating in a highly regulated environment, we at Capital One have learnings, insight, and best practices to share for creating a well-managed AI/ML environment. This includes my team’s newest research in the pursuit of explainable and transparent AI that we refer to as Global Attribution Models (GAM); working with multidisciplinary experts from different fields; establishing cross-functional internal working groups; and partnering with academics and other groups to ensure we are advancing the responsible use of AI/ML in a way that prioritizes the well-being of customers and humans more broadly. I will discuss this and present a broad framework for developing a Responsible AI program in my presentation.
Prerequisite knowledge16:9 screen for a standard powerpoint presentation. will also need microphone, confidence monitor, and remote clicker.
What you'll learn
Dave Castillo joined Capital One in 2018 and is a Managing Vice President in the Center for Machine Learning, an in-house center of excellence for machine learning, where he leads AI and machine learning initiatives in research and consulting across lines of business, as well as the development of tools, technologies, frameworks, and partnerships with industry and academia.
Prior to joining Capital One, Dave was Head of Data Science, Data Engineering, and Innovation for Early Warning where he was responsible for data ingestion, data transformation, profile assembly, feature extraction, and the development and deployment of machine learning models. Before Early Warning, Dave was CTO of Voltari, where he oversaw Cloud and Data Center Operations, Media Operations, Technology, Data Management Platform, and the Demand Side Platform (DSP) for real-time media buying and placement using automated self-training machine learning models. He also founded two startups specializing in automated predictive modeling for customer acquisition, retention, retargeting and segmentation in the digital and mobile marketing channels. He also held the position as Chief Software Engineer for Motorola’s IRIDIUM project and he began his career developing AI applications for NASA in vision, robotics, NLP, and case-based reasoning.
Dave holds a Ph.D., Masters, and Bachelor degrees in Engineering from University of Central Florida, Arizona State University, and the University of Arizona, respectively. He is also an active Adjunct Professor of Computer Science for the University of Maryland University College.
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