Learning When to Advise Human Decision Makers

Title:
Learning When to Advise Human Decision Makers

Abstract:
Artificial intelligence (AI) systems are increasingly used for providing advice to facilitate human decision making in a wide range of domains, such as healthcare, criminal justice, and finance. Motivated by limitations of the current practice where algorithmic advice is provided to human users as a constant element in the decision-making pipeline, in this paper we raise the question of when should algorithms provide advice? We propose a novel design of AI systems in which the algorithm interacts with the human user in a two-sided manner and aims to provide advice only when it is likely to be beneficial for the user in making their decision. The results of a large-scale experiment show that our advising approach manages to provide advice at times of need and to significantly improve human decision making compared to fixed, non-interactive, advising approaches. This approach has additional advantages in facilitating human learning, preserving complementary strengths of human decision makers, and leading to more positive responsiveness to the advice.
Joint work with Yiling Chen. 

Bio:
Gali Noti is a Postdoctoral Fellow at the Department of Computer Science at Cornell University. Prior to that, she was a Postdoctoral Fellow at the Harvard School of Engineering and Applied Sciences and at the School of Engineering and Computer Science of the Hebrew University of Jerusalem. Gali received her PhD from the School of Computer Science and Engineering and the Federmann Center for the Study of Rationality at the Hebrew University of Jerusalem, advised by Noam Nisan. Her research interests lie at the intersection of computer science and behavioral economics, studying how to design algorithmic systems that work well with human users.