{
  "id": "1705.09055",
  "version": "v1",
  "published": "2017-05-25T05:56:08.000Z",
  "updated": "2017-05-25T05:56:08.000Z",
  "title": "The cost of fairness in classification",
  "authors": [
    "Aditya Krishna Menon",
    "Robert C. Williamson"
  ],
  "categories": [
    "cs.LG"
  ],
  "abstract": "We study the problem of learning classifiers with a fairness constraint, with three main contributions towards the goal of quantifying the problem's inherent tradeoffs. First, we relate two existing fairness measures to cost-sensitive risks. Second, we show that for cost-sensitive classification and fairness measures, the optimal classifier is an instance-dependent thresholding of the class-probability function. Third, we show how the tradeoff between accuracy and fairness is determined by the alignment between the class-probabilities for the target and sensitive features. Underpinning our analysis is a general framework that casts the problem of learning with a fairness requirement as one of minimising the difference of two statistical risks.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2017-05-25T05:56:08.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "classification",
      "problems inherent tradeoffs",
      "fairness requirement",
      "existing fairness measures",
      "fairness constraint"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}