Stay Informed:

COVID-19 (coronavirus) information
Zoom Links: Zoom Help | Teaching with Zoom | Zoom Quick Guide

Soundness and Fairness in Data-Driven Decision Making

Speaker Name: 
Babak Salimi
Speaker Title: 
Postdoctoral Research Associate in Computer Science & Engineering
Speaker Organization: 
University of Washington, Seattle
Start Time: 
Friday, November 22, 2019 - 1:30pm
End Time: 
Friday, November 22, 2019 - 2:30pm
Professor Lise Getoor


Scaling and democratizing access to big data offers the alluring prospect of providing meaningful, actionable information that supports decision-making.   Today, data-driven decisions profoundly affect the course of our lives: whether to admit us to a particular school, offer us a job,  grant us a mortgage, etc. Therefore unfair, inconsistent, or faulty decision-making raises serious concerns about ethics and responsibility. This talk delves into two issues of soundness and fairness in decision making systems.  We show how causal inference techniques can help with both issues. 

We will show that analytical SQL queries supported by the mainstream business intelligence and analytics environments can lead to preparing results and wrong business decisions. We demonstrate a system which brings together techniques from data management and causal inference to automatically rewrite analytical SQL queries into complex causal queries that support sound decision making. 

We then show that sound decision making using causal inference is essential for reasoning about fairness and discrimination. We will see the existing popular notions of fairness in ML fail to distinguish between discriminatory, non-discriminatory and spurious correlations between sensitive attributes and outcome of learning algorithms. We will discuss a new notion of fairness that subsumes and improves on several previous definitions and can correctly distinguish fairness violations and non-violations. We will then present an approach to removing discrimination by repairing the training data in order to remove the effect of any inappropriate and discriminatory causal relationship between the protected attribute and classifier predictions


Babak Salimi is a postdoctoral research associate in Computer Science & Engineering at the University of Washington, Seattle, where he works with 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 a B.Sc. in Computer Engineering (2006) from Sharif University of Technology and Azad University of Mashhad, respectively. Babak's research interests span data management, causal inference, decision-making systems, algorithmic fairness and responsible data science.
Event Type: