3 papers
cs.LG2021
Leave-one-out Unfairness
Emily Black, Matt Fredrikson
We introduce leave-one-out unfairness, which characterizes how likely a model's prediction for an individual will change due to the inclusion or removal of a single other person in…
cs.LG2019
FlipTest: Fairness Testing via Optimal Transport
Emily Black, Samuel Yeom, Matt Fredrikson
We present FlipTest, a black-box technique for uncovering discrimination in classifiers. FlipTest is motivated by the intuitive question: had an individual been of a different prot…
cs.LG2018
Feature-Wise Bias Amplification
Klas Leino, Emily Black, Matt Fredrikson +2
We study the phenomenon of bias amplification in classifiers, wherein a machine learning model learns to predict classes with a greater disparity than the underlying ground truth.…