58 citations · 70 across the 6 of their papers we have counts for
6 papers
Algorithmic Fairness and Vertical Equity: Income Fairness with IRS Tax Audit Models
Emily Black, Hadi Elzayn, Alexandra Chouldechova +2
This study examines issues of algorithmic fairness in the context of systems that inform tax audit selection by the United States Internal Revenue Service (IRS). While the field of…
Reduced-bias estimation of the residual dependence index with unnamed marginals
Jennifer Israelsson, Emily Black, Claudia Neves +1
This paper addresses important weaknesses in current methodology for the estimation of multivariate extreme event distributions. The estimation of the residual dependence index $η\…
Selective Ensembles for Consistent Predictions
Emily Black, Klas Leino, Matt Fredrikson
Recent work has shown that models trained to the same objective, and which achieve similar measures of accuracy on consistent test data, may nonetheless behave very differently on…
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…
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…
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.…