Abstract: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 … Show more
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