  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. Furthermore, we show that it is
competitive with standard baselines over various datasets including ones used
in natural language processing and computer vision.
  FairGrad is available as a PyPI package at -
https://pypi.org/project/fairgrad
