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The AI experts at Monitaur explain the foundations of safe and resilient modeling systems behind the AI that impacts our lives and our businesses in our podcast, The AI Fundamentalists.
Episode 10. Joshua Pyle joins us in a discussion about managing bias in the actuarial sciences. Together with Andrew's and Sid's perspectives from both the economic and data science fields, this trio delivers an interdisciplinary conversation about bias that you'll only find here.
Dr. Andrew Clark is Monitaur’s co-founder and Chief Technology Officer. A trusted domain expert on the topic of ML auditing and assurance, Andrew built and deployed ML auditing solutions at Capital One. He has contributed to ML auditing education and standards at organizations including ISACA and ICO in the UK. He currently serves as a key contributor to ISO AI Standards and the NIST AI Risk Management framework. Prior to Monitaur, he also served as an economist and modeling advisor for several very prominent crypto-economic projects while at Block Science.
Andrew received a B.S. in Business Administration with a concentration in Accounting, Summa Cum Laude, from the University of Tennessee at Chattanooga, an M.S. in Data Science from Southern Methodist University, and a Ph.D. in Economics from the University of Reading. He also holds the Certified Analytics Professional and American Statistical Association Graduate Statistician certifications. Andrew is a professionally trained concert trumpeter and Team USA triathlete.
Sid Mangalik is a Ph.D. candidate in natural language processing at Stony Brook University working with Andy Schwartz. His previous studies focused on the intersection of artificial intelligence and psychology, where he worked with Ritwik Banerjee on language use in scientific writing and among abusers. Sid's previous experience also includes a role at Capital One as a Data Engineer.
His current research is working on identifying connections between community-level language and psychological outcomes. He is a Research Scientist at Monitaur, a start-up focused on making machine learning models fairer, safer, and more transparent.
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