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Die gebr\u00e4uchlichste Definition, wie sie in der Kognitionswissenschaft als mentale F\u00e4higkeit gegeben ist, enth\u00e4lt unter anderem die F\u00e4higkeit, abstrakt, logisch und schlussfolgernd zu denken und gegebene Probleme der realen Welt zu l\u00f6sen. Ein aktuelles Thema in der KI ist es, herauszufinden, ob und inwieweit Algorithmen in der Lage sind, solches abstraktes Denken und Schlussfolgern \u00e4hnlich wie Menschen zu erlernen \u2013 oder ob das Lernergebnis auf rein statistischer Korrelation beruht. In diesem Beitrag stellen wir eine von uns entwickelte frei verf\u00fcgbare, universelle und erweiterbare experimentelle Testumgebung vor. Diese \u201eKandinsky Patterns\u201c (<jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/human-centered.ai\/project\/kandinsky-patterns\">https:\/\/human-centered.ai\/project\/kandinsky-patterns<\/jats:ext-link>, <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/www.youtube.com\/watch?v=UuiV0icAlRs\">https:\/\/www.youtube.com\/watch?v=UuiV0icAlRs<\/jats:ext-link>), benannt nach dem russischen Maler und Kunsttheoretiker Wassily Kandinsky (1866\u20131944), stellen eine Art \u201eSchweizer Messer\u201c zum Studium der genannten Problemstellungen dar. Das Gebiet, dass diese Problemstellungen behandelt wird \u201eexplainable AI\u201c (xAI) genannt. Erkl\u00e4rbarkeit\/Interpretierbarkeit hat das Ziel, menschlichen Experten zu erm\u00f6glichen, zugrundeliegende Erkl\u00e4rungsfaktoren \u2013 die Kausalit\u00e4t \u2013 zu verstehen, also <jats:italic>warum<\/jats:italic> eine KI-Entscheidung getroffen wurde, und so den Weg f\u00fcr eine transparente und verifizierbare KI zu ebnen.<\/jats:p>","DOI":"10.1365\/s40702-020-00586-y","type":"journal-article","created":{"date-parts":[[2020,1,30]],"date-time":"2020-01-30T02:02:26Z","timestamp":1580349746000},"page":"33-45","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Verbinden von Nat\u00fcrlicher und K\u00fcnstlicher Intelligenz: eine experimentelle Testumgebung f\u00fcr Explainable AI (xAI)","Combining Natural and Artificial Intelligence: An Experimental Test Environment for Explainable AI (xAI)"],"prefix":"10.1365","volume":"57","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6786-5194","authenticated-orcid":false,"given":"Andreas","family":"Holzinger","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Heimo","family":"M\u00fcller","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"93","published-online":{"date-parts":[[2020,1,30]]},"reference":[{"key":"586_CR46","doi-asserted-by":"publisher","first-page":"294","DOI":"10.1007\/3-540-39200-9_18","volume-title":"Advances in Cryptology-Eurocrypt 2003","author":"L Von Ahn","year":"2003","unstructured":"Von Ahn\u00a0L, Blum\u00a0M, Hopper\u00a0NJ, Langford\u00a0J (2003) CAPTCHA: using hard AI problems for security. 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