381 citations · 1.4k across the 100 of their papers we have counts for
5 papers · 1 filter
Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness
Michael Kearns, Seth Neel, Aaron Roth +1
The most prevalent notions of fairness in machine learning are statistical definitions: they fix a small collection of pre-defined groups, and then ask for parity of some statistic…
Strategic Classification from Revealed Preferences
Jinshuo Dong, Aaron Roth, Zachary Schutzman +2
We study an online linear classification problem, in which the data is generated by strategic agents who manipulate their features in an effort to change the classification outcome…
Accuracy First: Selecting a Differential Privacy Level for Accuracy-Constrained ERM
Katrina Ligett, Seth Neel, Aaron Roth +2
Traditional approaches to differential privacy assume a fixed privacy requirement for a computation, and attempt to maximize the accuracy of the computation subject to the priv…
Fairness Incentives for Myopic Agents
Sampath Kannan, Michael Kearns, Jamie Morgenstern +4
We consider settings in which we wish to incentivize myopic agents (such as Airbnb landlords, who may emphasize short-term profits and property safety) to treat arriving clients fa…
Competing Bandits: Learning under Competition
Yishay Mansour, Aleksandrs Slivkins, Zhiwei Steven Wu
Most modern systems strive to learn from interactions with users, and many engage in exploration: making potentially suboptimal choices for the sake of acquiring new information. W…