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Framed as a problem of classification, a random sample of 45 definitions from various text sources was subjected to a qualitative content analysis. The criteria found are concluded in five dimensions, namely (1) <jats:italic>learning ability<\/jats:italic>, (2) <jats:italic>human likeness<\/jats:italic>, (3) <jats:italic>state of \u201cmind\u201d<\/jats:italic>, (4) <jats:italic>complexity of the problem<\/jats:italic>, and (5) <jats:italic>successfulness<\/jats:italic>. Further, the results support the view that there is no consensus neither on <jats:italic>which<\/jats:italic> of these criteria are crucial to define AI nor on <jats:italic>how<\/jats:italic> these criteria must be fulfilled. By opposing the frequencies of the dimensions found with the metadata collected, it can be seen that most of these, e.g., country, scientific field, or gender of the author, are statistically independent of content variables, while the medium in which the definition was published shows a strong correlation. Since different mediums target different purposes and different readers, it must be taken into account that writing a definition of AI is to be seen in the context of its distribution area and its goal.<\/jats:p>","DOI":"10.1007\/s00146-023-01643-y","type":"journal-article","created":{"date-parts":[[2023,3,24]],"date-time":"2023-03-24T18:02:48Z","timestamp":1679680968000},"page":"1905-1919","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Hey Alexa, why are you called intelligent? 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