Jure Leskovec: 2017 Plenary Session

 

Tuesday, April 11, 2017
Location: McCaw Hall, Arrillaga Alumni Center

"Human Decisions and Machine Predictions"

4:30PM

Abstract:

Evaluating whether machines improve on human performance is one of the central questions of machine learning. In this talk we present a novel framework for evaluating the performance of predictive models on selectively labeled data. We demonstrate the framework on criminal court judges deciding whether to jail a defendant. We show that machine learning can reduce crime by up to 24.8% with no change in jailing, or reduce jail populations by 42.0% with no increase in crime. Such gains can be achieved while simultaneously reducing racial disparities as well as reducing all categories of crime, including the most violent. We also develop methods to identify reasons for judicial error---judges overfit the unobserved "noise". These findings suggest that machine learning and prediction tools can be used to understand and improve human decisions.


Bio:

Jure Leskovec is an associate professor of Computer Science at Stanford University where he is a member of the InfoLab and the AI lab. Jure joined the department in September 2009.

Jure is also working as the Chief Scientists at Pinterest, where he is focusing on machine learning problems. He co-founded of a machine learning startup Kosei, which was acquired by Pinterest.

In 2008/09, Jure was a postdoctoral researcher at Cornell University working with Jon Kleinberg and Dan Huttenlocher. he completed his Ph.D. in Machine Learning Department, School of Computer Science at Carnegie Mellon University under the supervision of Christos Faloutsos in 2008.

Jure did his undergraduate degree in computer science at University of Ljubljana, Slovenia in 2004.

He also works with the Artificial Intelligence Laboratory,Jozef Stefan Institute, Ljubljana, Slovenia.