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.

在实现算法公平性方面存在多个实际挑战，包括数据集中受保护属性的可用性或可靠性。本研究首次对公平分类算法进行了头对头的研究，以比较基于属性、容忍噪声和盲目属性的算法在预测性和公平性两个方面的表现。我们通过对四个真实数据集和合成扰动的案例研究来评估这些算法，发现盲目属性和容忍噪声的公平分类器在受保护属性存在噪声的情况下，可能实现与基于属性的算法类似的性能水平。然而，在实践中实现这些算法需要谨慎处理。本研究为在受保护属性存在噪声或部分可用的场景中使用公平分类算法提供了实践的启示。