Limitations of Pinned AUC for Measuring Unintended Bias
Machine Learning
2019-03-07 v1 Machine Learning
Abstract
This report examines the Pinned AUC metric introduced and highlights some of its limitations. Pinned AUC provides a threshold-agnostic measure of unintended bias in a classification model, inspired by the ROC-AUC metric. However, as we highlight in this report, there are ways that the metric can obscure different kinds of unintended biases when the underlying class distributions on which bias is being measured are not carefully controlled.
Cite
@article{arxiv.1903.02088,
title = {Limitations of Pinned AUC for Measuring Unintended Bias},
author = {Daniel Borkan and Lucas Dixon and John Li and Jeffrey Sorensen and Nithum Thain and Lucy Vasserman},
journal= {arXiv preprint arXiv:1903.02088},
year = {2019}
}