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Babak Salimi: Causal Inference for Responsible Data Science
April 14, 2020 @ 11:00 am - 12:00 pm
Scaling and democratizing access to big data promises to provide meaningful, actionable information that supports decision-making. Today, data-driven decisions profoundly affect the course of our lives, such as whether to admit applicants to a particular school, offer them a job, or grant them a mortgage. Unfair, inconsistent, or faulty decision-making raises serious concerns about ethics and responsibility. For example, we may know that our training data is biased, but how do we avoid propagating discrimination when we use this data? How do we avoid incorrect, spurious and non-reproducible findings? How can we curate and expose existing data to make it “safe” for informed decision making?
In this talk,I describe how we can combine techniques from causal inference and data management to develop systems and algorithms that help answer some of these questions. Many existing popular notions of fairness in ML fail to distinguish between discriminatory, non-discriminatory and spurious correlations between sensitive attributes and outcomes of learning algorithms. I present a new notion of fairness that subsumes and improves upon previous definitions and correctly distinguishes between fairness violations and non-violations. Further, I describe an approach to removing discrimination by repairing training data in order to remove the effects of any inappropriate and/or discriminatory causal relationships between a protected attribute and classifier predictions. Analytical SQL queries supported by mainstream business intelligence and analytics environments can lead to perplexing observations and incorrect business decisions. I describe a system that automatically rewrites analytical SQL queries into complex causal queries that support decision-making.
Babak Salimi is a postdoctoral research associate in Computer Science & Engineering at the University of Washington, Seattle, where he works with Prof. Dan Suciu and the Database Group. He received his Ph.D. from the School of Computer Science at Carleton University in Ottawa, Canada and his M.Sc. in Computation Theory (2009) and B.Sc. in Computer Engineering (2006) from Sharif University of Technology and Azad University of Mashhad, respectively. Salimi’s research interests span data management, causal inference, decision-making systems, algorithmic fairness and responsible data science.
Zoom Link: https://uci.zoom.us/j/232157494