278 citations · 618 across the 32 of their papers we have counts for
4 papers · 1 filter
Group-based Fair Learning Leads to Counter-intuitive Predictions
Ofir Nachum, Heinrich Jiang
A number of machine learning (ML) methods have been proposed recently to maximize model predictive accuracy while enforcing notions of group parity or fairness across sub-populatio…
Wasserstein Fair Classification
Ray Jiang, Aldo Pacchiano, Tom Stepleton +2
We propose an approach to fair classification that enforces independence between the classifier outputs and sensitive information by minimizing Wasserstein-1 distances. The approac…
Minimum-Margin Active Learning
Heinrich Jiang, Maya Gupta
We present a new active sampling method we call min-margin which trains multiple learners on bootstrap samples and then chooses the examples to label based on the candidates' minim…
Identifying and Correcting Label Bias in Machine Learning
Heinrich Jiang, Ofir Nachum
Datasets often contain biases which unfairly disadvantage certain groups, and classifiers trained on such datasets can inherit these biases. In this paper, we provide a mathematica…