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In addition, internally used metrics\u00a0have biases that might not match existing structures in the data. The habilitation thesis\u00a0presents an alternative solution approach by deriving explanations from high\u00a0dimensional\u00a0structures in the data rather than from predetermined classifications.\u00a0Typically, the detection of such density- or distance-based structures in data has so far\u00a0entailed the challenges of choosing appropriate algorithms and their parameters,\u00a0which adds a\u00a0considerable\u00a0amount of complex\u00a0decision-making options for the HIL.\u00a0Central steps of the solution approach are a parameter-free methodology for the\u00a0estimation and visualization of probability density functions (PDFs); followed by a\u00a0hypothesis for selecting an appropriate distance metric independent of the data context\u00a0in combination with projection-based clustering (PBC). PBC allows for subsequent\u00a0interactive identification of separable structures in the data. Hence, the HIL does not\u00a0need deep knowledge of the underlying algorithms to identify structures in data.\u00a0The complete data-driven\u00a0XAI approach involving the HIL\u00a0is based on a decision tree guided by distance-based\u00a0structures in data (DSD). This data-driven XAI\u00a0shows initial success in the application to\u00a0multivariate time series and non-sequential high-dimensional data. It generates\u00a0meaningful and relevant explanations that are evaluated by Grice\u2019s maxims.<\/jats:p>","DOI":"10.1007\/s13218-022-00782-6","type":"journal-article","created":{"date-parts":[[2022,11,22]],"date-time":"2022-11-22T23:05:04Z","timestamp":1669158304000},"page":"297-301","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Identification of Explainable Structures in Data with a Human-in-the-Loop"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9542-5543","authenticated-orcid":false,"given":"Michael C.","family":"Thrun","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,11,21]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Ultsch A, Korus D (1995) Integration of neural networks and knowledge-based systems. In: International Conference on Neural Networks. Perth, Australia. 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