{
  "id": "2205.13662",
  "version": "v1",
  "published": "2022-05-26T22:53:41.000Z",
  "updated": "2022-05-26T22:53:41.000Z",
  "title": "Explaining Preferences with Shapley Values",
  "authors": [
    "Robert Hu",
    "Siu Lun Chau",
    "Jaime Ferrando Huertas",
    "Dino Sejdinovic"
  ],
  "categories": [
    "stat.ML",
    "cs.LG",
    "stat.ME"
  ],
  "abstract": "While preference modelling is becoming one of the pillars of machine learning, the problem of preference explanation remains challenging and underexplored. In this paper, we propose \\textsc{Pref-SHAP}, a Shapley value-based model explanation framework for pairwise comparison data. We derive the appropriate value functions for preference models and further extend the framework to model and explain \\emph{context specific} information, such as the surface type in a tennis game. To demonstrate the utility of \\textsc{Pref-SHAP}, we apply our method to a variety of synthetic and real-world datasets and show that richer and more insightful explanations can be obtained over the baseline.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2022-05-26T22:53:41.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "shapley values",
      "explaining preferences",
      "shapley value-based model explanation framework",
      "appropriate value functions",
      "preference explanation remains challenging"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}