Fairness Risks for Group-conditionally Missing Demographics
Kaiqi Jiang Wenzhe Fan Mao Li Xinhua Zhang
Fairness-aware classification models have gained increasing attention in
recent years as concerns grow on discrimination against some demographic
groups. Most existing models require full knowledge of the sensitive features,
which can be impractical due to privacy, legal issues, and an individual's fear
of discrimination. The key challenge we will address is the group dependency of
the unavailability, e.g., people of some age range may be more reluctant to
reveal their age. Our solution augments general fairness risks with
probabilistic imputations of the sensitive features, while jointly learning the
group-conditionally missing probabilities in a variational auto-encoder. Our
model is demonstrated effective on both image and tabular datasets, achieving
an improved balance between accuracy and fairness.