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Wednesday, April 6 • 2:00pm - 4:30pm
Discovering Unwarranted Associations in Data-Driven Applications with the FairTest Testing Toolkit [D3]

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In today’s data-driven world, programmers routinely incorporate user data into complex algorithms, heuristics, and application pipelines. While often beneficial, this practice can have unintended and detrimental consequences, such as the discriminatory effects identified in Staples’ online pricing algorithm and the racially offensive labels recently found in Google’s image tagger.    We argue that such effects are bugs that should be tested for and debugged in a manner similar to functionality, performance, and security bugs. We developed FairTest, a testing toolkit that detects unwarranted associations between an algorithm’s outputs (e.g., prices or labels) and user subpopulations, including protected groups (e.g., defined by race or gender). FairTest reports any statistically significant associations to programmers as potential bugs, ranked by their strength and likelihood of being unintentional, rather than necessary effects.    In the demo, we will show how FairTest can be used to identify unfair disparate impact, offensive labeling, and disparate rates of algorithmic error in data-driven applications. For example, we will show how FairTest can reveal subtle biases against older populations in the distribution of error in a real predictive health application, and offensive racial labeling in an image tagger akin to Google's. 

Demo/Poster Presenter
avatar for Daniel Hsu

Daniel Hsu

Assistant Professor of Computer Science, Columbia Engineering
Daniel Hsu is an assistant professor in the Department of Computer Science and a member of the Data Science Institute, both at Columbia University. Previously, he was a postdoc at Microsoft Research New England, and the Departments of Statistics at Rutgers University and th... Read More →
avatar for Roxana Geambasu

Roxana Geambasu

Assistant Professor of Computer Science, Columbia Engineering
Roxana Geambasu is an assistant professor in the Computer Science Department at Columbia University. She is interested in computer systems in a broad sense, including distributed systems, the Web, security and privacy, operating systems, and databases. More specifically, her  current... Read More →
avatar for Vaggelis Atlidakis

Vaggelis Atlidakis

PhD Candidate in Computer Science, Columbia Engineering
Vaggelis is a Computer Science Ph.D. student at Columbia University in the city of New York. He is a member of the Software Systems Laboratory and his advisors are Roxana Geambasuand Jason Nieh. Before joining Columbia Vaggelis was a member of the European Organization for N... Read More →

Wednesday April 6, 2016 2:00pm - 4:30pm EDT
Roone Arledge Auditorium Lerner Hall, Columbia University 2920 Broadway, New York, NY 10040

Attendees (2)