Human Decisions and Machine Predictions
Event details
| Date | 08.02.2017 |
| Hour | 14:00 › 15:30 |
| Speaker |
Professor Jure Leskovec, Stanford University, Chief Scientist at Pinterest, USA Jure Leskovec <http://cs.stanford.edu/~jure> is Associate Professor of Computer Science at Stanford University and Chief Scientist at Pinterest. Computation over massive data is at the heart of his research and has applications in computer science, social sciences, economics, marketing, and healthcare. This research has won several awards including a Lagrange Prize, Microsoft Research Faculty Fellowship, the Alfred P. Sloan Fellowship, and numerous best paper awards. Leskovec received his bachelor's degree in computer science from University of Ljubljana, Slovenia, and his PhD in in machine learning from the Carnegie Mellon University and postdoctoral training at Cornell University. |
| Location |
IC Faculty, BC420, 4th floor
|
| Category | Conferences - Seminars |
Abstract
In this talk we examine how machine learning can be used to improve human decisions---in particular, on judges deciding whether to jail an arrestee pending resolution of their case. This is an ideal application because, by law, this decision must rely on judges’ prediction of what a defendant would do if released. There are several interesting methodological challenges in this domain. First, we must solve a selection problem: we do not observe what jailed defendants would do if they were released. Second, judges’ payoffs may involve more than minimizing crime risk. Algorithmic recommendations may reduce crime risk but may not improve the richer total welfare function. We develop a methodology to deal with these problems and find 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 to unobserved ‘noise’. These findings suggest that machine learning and prediction tools can be used to understand and improve human decisions. On the other hand, they illustrate how this must be a joint activity between the design of prediction algorithms and the development of an economic framework that focuses on payoffs, decisions and selection biases.
In this talk we examine how machine learning can be used to improve human decisions---in particular, on judges deciding whether to jail an arrestee pending resolution of their case. This is an ideal application because, by law, this decision must rely on judges’ prediction of what a defendant would do if released. There are several interesting methodological challenges in this domain. First, we must solve a selection problem: we do not observe what jailed defendants would do if they were released. Second, judges’ payoffs may involve more than minimizing crime risk. Algorithmic recommendations may reduce crime risk but may not improve the richer total welfare function. We develop a methodology to deal with these problems and find 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 to unobserved ‘noise’. These findings suggest that machine learning and prediction tools can be used to understand and improve human decisions. On the other hand, they illustrate how this must be a joint activity between the design of prediction algorithms and the development of an economic framework that focuses on payoffs, decisions and selection biases.
Practical information
- General public
- Free
- This event is internal
Organizer
- Dr. Olivier Verscheure, Swiss Data Science Center, SDSC
Contact
- Dr. Olivier Verscheure, Swiss Data Science Center, SDSC