{
  "id": "1811.11669",
  "version": "v1",
  "published": "2018-11-28T16:49:37.000Z",
  "updated": "2018-11-28T16:49:37.000Z",
  "title": "Towards Identifying and Managing Sources of Uncertainty in AI and Machine Learning Models - An Overview",
  "authors": [
    "Michael Kläs"
  ],
  "categories": [
    "cs.LG",
    "stat.ML"
  ],
  "abstract": "Quantifying and managing uncertainties that occur when data-driven models such as those provided by AI and machine learning methods are applied is crucial. This whitepaper provides a brief motivation and first overview of the state of the art in identifying and quantifying sources of uncertainty for data-driven components as well as means for analyzing their impact.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2018-11-28T16:49:37.000Z"
    }
  ],
  "analyses": {
    "subjects": [
      "68T01"
    ],
    "keywords": [
      "machine learning models",
      "managing sources",
      "uncertainty",
      "identifying",
      "data-driven components"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}