• Corpus ID: 226278250

Convergent Algorithms for (Relaxed) Minimax Fairness

@article{Diana2020ConvergentAF,
  title={Convergent Algorithms for (Relaxed) Minimax Fairness},
  author={Emily Diana and Wesley Gill and Michael Kearns and Krishnaram Kenthapadi and Aaron Roth},
  journal={ArXiv},
  year={2020},
  volume={abs/2011.03108}
}
We consider a recently introduced framework in which fairness is measured by worst-case outcomes across groups, rather than by the more standard $\textit{difference}$ between group outcomes. In this framework we provide provably convergent $\textit{oracle-efficient}$ learning algorithms (or equivalently, reductions to non-fair learning) for $\textit{minimax group fairness}$. Here the goal is that of minimizing the maximum loss across all groups, rather than equalizing group losses. Our… 

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