Proxy Fairness

Abstract

We consider the problem of improving fairness when one lacks access to a dataset labeled with protected groups, making it difficult to take advantage of strategies that can improve fairness but require protected group labels, either at training or runtime. To address this, we investigate improving fairness metrics for proxy groups, and test whether doing so results in improved fairness for the true sensitive groups. Results on benchmark and real-world datasets demonstrate that such a proxy fairness strategy can work well in practice. However, we caution that the effectiveness likely depends on the choice of fairness metric, as well as how aligned the proxy groups are with the true protected groups in terms of the constrained model parameters.

Cited by in corpus (8)

• Fairness without Demographics through Adversarially Reweighted Learning
• Noise-tolerant fair classification
• Fairness without the sensitive attribute via Causal Variational Autoencoder
• Equalized odds postprocessing under imperfect group information
• Fair Classification via Domain Adaptation: A Dual Adversarial Learning Approach
• Adversarial Reweighting for Speaker Verification Fairness
• Group fairness without demographics using social networks
• Balancing Competing Objectives with Noisy Data: Score-Based Classifiers for Welfare-Aware Machine Learning