Corpus ID: 211133323

Fast Fair Regression via Efficient Approximations of Mutual Information

@article{Steinberg2020FastFR,
  title={Fast Fair Regression via Efficient Approximations of Mutual Information},
  author={Daniel Steinberg and Alistair Reid and Simon Timothy O'Callaghan and Finnian Lattimore and Lachlan McCalman and Tib{\'e}rio S. Caetano},
  journal={ArXiv},
  year={2020},
  volume={abs/2002.06200}
}
  • Daniel Steinberg, Alistair Reid, +3 authors Tibério S. Caetano
  • Published in ArXiv 2020
  • Mathematics, Computer Science
  • Most work in algorithmic fairness to date has focused on discrete outcomes, such as deciding whether to grant someone a loan or not. In these classification settings, group fairness criteria such as independence, separation and sufficiency can be measured directly by comparing rates of outcomes between subpopulations. Many important problems however require the prediction of a real-valued outcome, such as a risk score or insurance premium. In such regression settings, measuring group fairness… CONTINUE READING

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