  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.
