International Business Machines Corporation

University of Michigan

Yuekai Sun

Sohini Upadhyay

Mikhail Yurochkin

Fan Zhang

Debarghya Mukherjee

Alexander Vargo

Moulinath Banerjee

Amanda Ruth Garcia Bower

Seyed Hamid Eftekhari

Obtain a first data set, a second data set, and a machine learning model. Construct a sensitive subspace of the first data set that defines a fair metric for distance among elements of the first data set. Fairly train the machine learning model on the first data set using a distributionally robust optimization approach based on the fair metric. Produce an individually fair set of labels by applying the fairly trained machine learning model to the second data set. Allocate a resource according to the individually fair set of labels.

Training individually fair machine learning algorithms via distributionally robust optimization

The present invention provides techniques for learning Mahalanobis distance similarity metrics from data for individually fair machine learning models. In one aspect, a method for learning a fair Mahalanobis distance similarity metric includes: obtaining data with similarity annotations; selecting, based on the data obtained, a model for learning a Mahalanobis covariance matrix Σ; and learning the Mahalanobis covariance matrix Σ from the data using the model selected, wherein the Mahalanobis covariance matrix Σ fully defines the fair Mahalanobis distance similarity metric.

Learning Mahalanobis distance metrics from data

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Ann Arbor, MI, United States of America

Yuekai Sun