Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment

@article{Zafar2017FairnessBD,
  title={Fairness Beyond Disparate Treatment \& Disparate Impact: Learning Classification without Disparate Mistreatment},
  author={M. B. Zafar and Isabel Valera and M. Gomez-Rodriguez and K. Gummadi},
  journal={Proceedings of the 26th International Conference on World Wide Web},
  year={2017}
}
Automated data-driven decision making systems are increasingly being used to assist, or even replace humans in many settings. These systems function by learning from historical decisions, often taken by humans. In order to maximize the utility of these systems (or, classifiers), their training involves minimizing the errors (or, misclassifications) over the given historical data. However, it is quite possible that the optimally trained classifier makes decisions for people belonging to… Expand
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