{
  "id": "1911.07749",
  "version": "v1",
  "published": "2019-11-15T08:14:26.000Z",
  "updated": "2019-11-15T08:14:26.000Z",
  "title": "On the computation of counterfactual explanations -- A survey",
  "authors": [
    "André Artelt",
    "Barbara Hammer"
  ],
  "comment": "In progress. arXiv admin note: text overlap with arXiv:1908.00735",
  "categories": [
    "cs.LG",
    "cs.AI",
    "stat.ML"
  ],
  "abstract": "Due to the increasing use of machine learning in practice it becomes more and more important to be able to explain the prediction and behavior of machine learning models. An instance of explanations are counterfactual explanations which provide an intuitive and useful explanations of machine learning models. In this survey we review model-specific methods for efficiently computing counterfactual explanations of many different machine learning models and propose methods for models that have not been considered in literature so far.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2019-11-15T08:14:26.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "machine learning models",
      "computation",
      "review model-specific methods",
      "efficiently computing counterfactual explanations",
      "useful explanations"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}