When Fair Classification Meets Noisy Protected Attributes

TL;DR

This paper compares different fair classification algorithms, including attribute-reliant, noise-tolerant, and attribute-blind methods, to evaluate their effectiveness in real-world scenarios with noisy or missing protected attributes.

Contribution

It is the first comprehensive head-to-head comparison of these algorithms across predictivity and fairness, providing practical insights for real-world applications.

Findings

Attribute-blind and noise-tolerant classifiers can match attribute-reliant performance.

Noisy protected attributes do not necessarily degrade fairness or accuracy.

Careful implementation is required when applying these algorithms in practice.

Abstract

The operationalization of algorithmic fairness comes with several practical challenges, not the least of which is the availability or reliability of protected attributes in datasets. In real-world contexts, practical and legal impediments may prevent the collection and use of demographic data, making it difficult to ensure algorithmic fairness. While initial fairness algorithms did not consider these limitations, recent proposals aim to achieve algorithmic fairness in classification by incorporating noisiness in protected attributes or not using protected attributes at all. To the best of our knowledge, this is the first head-to-head study of fair classification algorithms to compare attribute-reliant, noise-tolerant and attribute-blind algorithms along the dual axes of predictivity and fairness. We evaluated these algorithms via case studies on four real-world datasets and synthetic perturbations. Our study reveals that attribute-blind and noise-tolerant fair classifiers can potentially achieve similar level of performance as attribute-reliant algorithms, even when protected attributes are noisy. However, implementing them in practice requires careful nuance. Our study provides insights into the practical implications of using fair classification algorithms in scenarios where protected attributes are noisy or partially available.