799 citations · 2.2k across the 20 of their papers we have counts for
22 papers
Reconciling the accuracy-diversity trade-off in recommendations
Kenny Peng, Manish Raghavan, Emma Pierson +2
In recommendation settings, there is an apparent trade-off between the goals of accuracy (to recommend items a user is most likely to want) and diversity (to recommend items repres…
Auditing for Human Expertise
Rohan Alur, Loren Laine, Darrick K. Li +3
High-stakes prediction tasks (e.g., patient diagnosis) are often handled by trained human experts. A common source of concern about automation in these settings is that experts may…
Simplistic Collection and Labeling Practices Limit the Utility of Benchmark Datasets for Twitter Bot Detection
Chris Hays, Zachary Schutzman, Manish Raghavan +2
Accurate bot detection is necessary for the safety and integrity of online platforms. It is also crucial for research on the influence of bots in elections, the spread of misinform…
The Right to be an Exception to a Data-Driven Rule
Sarah H. Cen, Manish Raghavan
Data-driven tools are increasingly used to make consequential decisions. They have begun to advise employers on which job applicants to interview, judges on which defendants to gra…
The Challenge of Understanding What Users Want: Inconsistent Preferences and Engagement Optimization
Jon Kleinberg, Sendhil Mullainathan, Manish Raghavan
Online platforms have a wealth of data, run countless experiments and use industrial-scale algorithms to optimize user experience. Despite this, many users seem to regret the time…
Stochastic Model for Sunk Cost Bias
Jon Kleinberg, Sigal Oren, Manish Raghavan +1
We present a novel model for capturing the behavior of an agent exhibiting sunk-cost bias in a stochastic environment. Agents exhibiting sunk-cost bias take into account the effort…