Computer Science Seminar: Babak Salimi

This event is open to all Illinois Tech faculty and students. 

Abstract

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, Babak Salimi describes 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. Salimi will present a new notion of fairness that subsumes and improves upon previous definitions and correctly distinguishes between fairness violations and non-violations. Further, Salimi will 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. Salimi will describe a system that automatically rewrites analytical SQL queries into complex causal queries that support decision-making.

Bio

Salimi is a postdoctoral research associate in computer science and engineering at the University of Washington in Seattle, where he works with Professor Dan Suciu and the Database Group. Salimi 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.