{
  "id": "2005.00130",
  "version": "v1",
  "published": "2020-04-30T22:34:37.000Z",
  "updated": "2020-04-30T22:34:37.000Z",
  "title": "Hide-and-Seek: A Template for Explainable AI",
  "authors": [
    "Thanos Tagaris",
    "Andreas Stafylopatis"
  ],
  "comment": "24 pages, 14 figures. Submitted on a special issue for Explainable AI, on Elsevier's \"Artificial Intelligence\"",
  "categories": [
    "cs.LG",
    "cs.AI",
    "stat.ML"
  ],
  "abstract": "Lack of transparency has been the Achilles heal of Neural Networks and their wider adoption in industry. Despite significant interest this shortcoming has not been adequately addressed. This study proposes a novel framework called Hide-and-Seek (HnS) for training Interpretable Neural Networks and establishes a theoretical foundation for exploring and comparing similar ideas. Extensive experimentation indicates that a high degree of interpretability can be imputed into Neural Networks, without sacrificing their predictive power.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2020-04-30T22:34:37.000Z"
    }
  ],
  "analyses": {
    "subjects": [
      "62M45",
      "I.2.6"
    ],
    "keywords": [
      "explainable ai",
      "hide-and-seek",
      "despite significant interest",
      "wider adoption",
      "achilles heal"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 24,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}