JP Morgan Tech Talk: Fair and explainable AI/ML for financial services

Abstract: The financial services industry has many needs for fairness and explainability in artificial intelligence and machine learning, which stem from considerations of transparency, ethics, regulatory compliance, and risk management. [1] For example, banks must prove that the way that they approve mortgages comply with fair lending laws and promote community development, while at the same time managing risk appropriately. [2] These needs translate directly onto AI/ML solutions being developed for these business needs. In this talk, I introduce two research challenges that arise from the unique mix of business needs and regulatory constraints. First, we develop new methods for measuring bias in decision processes where labels for protected class membership cannot be observed [3, 4]. Second, we review the many definitions of fairness [5] and existing results on which definitions are mutually incompatible [6,7], and present our latest results exploring fairness-fairness and fairness-performance trade-offs.

Wednesday November 6, 2019
12:00 PM- 1:00PM
MIT CSAIL Stata Center, Star Conf. Room D463

Lunch will be provided

Please register or email Callie Mathews- cmathews@csail.mit.edu to confirm attendance.

https://www.eventbrite.com/e/csail-alliance-jp-morgan-tech-talk-tickets-79769983167