  Algorithmic fairness has become an important machine learning problem,
especially for mission-critical Web applications. This work presents a
self-supervised model, called DualFair, that can debias sensitive attributes
like gender and race from learned representations. Unlike existing models that
target a single type of fairness, our model jointly optimizes for two fairness
criteria - group fairness and counterfactual fairness - and hence makes fairer
predictions at both the group and individual levels. Our model uses contrastive
loss to generate embeddings that are indistinguishable for each protected
group, while forcing the embeddings of counterfactual pairs to be similar. It
then uses a self-knowledge distillation method to maintain the quality of
representation for the downstream tasks. Extensive analysis over multiple
datasets confirms the model's validity and further shows the synergy of jointly
addressing two fairness criteria, suggesting the model's potential value in
fair intelligent Web applications.
