  Feature selection is a prevalent data preprocessing paradigm for various
learning tasks. Due to the expensive cost of acquiring supervision information,
unsupervised feature selection sparks great interests recently. However,
existing unsupervised feature selection algorithms do not have fairness
considerations and suffer from a high risk of amplifying discrimination by
selecting features that are over associated with protected attributes such as
gender, race, and ethnicity. In this paper, we make an initial investigation of
the fairness-aware unsupervised feature selection problem and develop a
principled framework, which leverages kernel alignment to find a subset of
high-quality features that can best preserve the information in the original
feature space while being minimally correlated with protected attributes.
Specifically, different from the mainstream in-processing debiasing methods,
our proposed framework can be regarded as a model-agnostic debiasing strategy
that eliminates biases and discrimination before downstream learning algorithms
are involved. Experimental results on multiple real-world datasets demonstrate
that our framework achieves a good trade-off between utility maximization and
fairness promotion.
