Original paper
Fair Transfer Learning with Missing Protected Attributes
Published: Jan 27, 2019
Abstract
Risk assessment is a growing use for machine learning models. When used in high-stakes applications, especially ones regulated by anti-discrimination laws or governed by societal norms for fairness, it is important to ensure that learned models do not propagate and scale any biases that may exist in training data. In this paper, we add on an additional challenge beyond fairness: unsupervised domain adaptation to covariate shift between a source...
Paper Details
Title
Fair Transfer Learning with Missing Protected Attributes
Published Date
Jan 27, 2019
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