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While both paradigms coexist in practice, we critically discuss the distinctive epistemological and ethical implications of machine learning's \u201cexemplary\u201d type of authority. To capture its logic, we compare it to computer programming rules that date to the middle of the 20th century, showing how rules and examples have regulated human conduct in significantly different ways. In contrast to the highly constructed, explicit, and prescriptive form of authority imposed by programming rules, machine learning models are trained using data that has been made into examples. These examples elicit norms in an implicit, emergent manner to make prediction and classification possible. We analyze three ways that examples are produced in machine learning: labeling, feature engineering, and scaling. We use the phrase \u201cartificial naturalism\u201d to characterize the tensions of this type of authority, in which examples sit ambiguously between data and norm.<\/jats:p>","DOI":"10.1177\/20539517231188725","type":"journal-article","created":{"date-parts":[[2023,9,13]],"date-time":"2023-09-13T03:42:58Z","timestamp":1694576578000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":31,"title":["From rules to examples: Machine learning's type of authority"],"prefix":"10.1177","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3159-4131","authenticated-orcid":false,"given":"Alexander","family":"Campolo","sequence":"first","affiliation":[{"name":"Department of Geography, Durham University, Durham, United Kingdom"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7938-2608","authenticated-orcid":false,"given":"Katia","family":"Schwerzmann","sequence":"additional","affiliation":[{"name":"Institut f\u00fcr Medienwissenschaft, Ruhr-Universit\u00e4t Bochum, Bochum, Germany"}]}],"member":"179","published-online":{"date-parts":[[2023,9,13]]},"reference":[{"key":"bibr1-20539517231188725","unstructured":"23andMe\n                      (2020a) Ancestry composition guide. 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