560 citations · 2.1k across the 38 of their papers we have counts for
Showing 2017 · cs.LGShow all
2 papers · 2 filters
cs.LG2017★ 381 cited
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…
cs.LG2017★ 194 cited
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…