FairGrad: Fairness Aware Gradient Descent

Understand

We address the problem of group fairness in classification, where the objective is to learn models that do not unjustly discriminate against subgroups of the population.

• Most existing approaches are limited to simple binary tasks or involve difficult to implement training mechanisms which reduces their practical applicability.
• In this paper, we propose FairGrad, a method to enforce fairness based on a re-weighting scheme that iteratively learns group specific weights based on whether they are advantaged or not.
• FairGrad is easy to implement, accommodates various standard fairness definitions, and comes with minimal overhead.