Fairness in Financial Services (via Zoom)

Abstract: The financial services industry needs fairness and explainability in artificial intelligence and machine learning, arising from considerations of transparency, ethics, regulatory compliance, and risk management [arXiv:1809.04684,2010.04827]. In this talk, I introduce some of the research challenges that arise from developing models for regulated decision making. We studied how to measure bias in models when labels for protected class membership cannot be observed [arXiv:1811.11154], or cannot be revealed for privacy reasons [arXiv:2010.04840]. We also generalized existing results on fairness-fairness and fairness-performance impossibilities and trade-offs using a convex programming formalism [arXiv:2004.03424]. Finally, we will discuss some open problems and ongoing areas of research within JPMorgan AI Research.

Bio: Jiahao Chen is a Senior Vice President and AI Research Lead at JPMorgan AI Research in New York, with research focusing on explainability and fairness in machine learning, as well as semantic knowledge management. He was previously a Senior Manager of Data Science at Capital One focusing on machine learning research for credit analytics and retail operations, and a former Research Scientist at MIT CSAIL where he co-founded and led the Julia Lab, focusing on applications of the Julia programming language to data science, scientific computing, and machine learning. Jiahao has organized the JuliaCon conference and workshops at NeurIPS, SIAM CSE, and the American Chemical Society National Meetings. Jiahao holds a PhD in chemical physics, a MS in applied mathematics, and a BS in chemistry, all from UIUC. He was formerly a postdoctoral associate at MIT, a visiting scholar at Ritsumeikan University in Japan, and a member of technical staff at DSO National Laboratories in Singapore. JPMorgan AI Research is actively recruiting PhD interns and full-time research positions in the US and UK. https://jpmorgan.com/ai