381 citations · 2.3k across the 89 of their papers we have counts for
4 papers · 2 filters
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…
A Convex Framework for Fair Regression
Richard Berk, Hoda Heidari, Shahin Jabbari +5
We introduce a flexible family of fairness regularizers for (linear and logistic) regression problems. These regularizers all enjoy convexity, permitting fast optimization, and the…
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…