• Corpus ID: 221470392

Comparing Fair Ranking Metrics

@article{Raj2020ComparingFR,
  title={Comparing Fair Ranking Metrics},
  author={Amifa Raj and Connor Wood and Ananda Montoly and Michael D. Ekstrand},
  journal={ArXiv},
  year={2020},
  volume={abs/2009.01311}
}
Ranking is a fundamental aspect of recommender systems. However, ranked outputs can be susceptible to various biases; some of these may cause disadvantages to members of protected groups. Several metrics have been proposed to quantify the (un)fairness of rankings, but there has not been to date any direct comparison of these metrics. This complicates deciding what fairness metrics are applicable for specific scenarios, and assessing the extent to which metrics agree or disagree. In this paper… 

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