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The current research aims at reconciling conflicting findings on \u2018algorithmic aversion\u2019 in the literature. It does so by investigating algorithmic aversion while controlling for two important characteristics that are often associated with ADM: increased benefits (monetary and accuracy) and decreased user control. Across three high-powered (<jats:italic>N<\/jats:italic><jats:sub>total<\/jats:sub>\u2009=\u20091192), preregistered 2 (agent: algorithm\/human)\u2009\u00d7\u20092 (benefits: high\/low)\u2009\u00d7\u20092 (control: user control\/no control) between-subjects experiments, and two domains (finance and dating), the results were quite consistent: there is little evidence for a default aversion against algorithms and in favor of human decision makers. Instead, users accept or reject decisions and decisional agents based on their predicted benefits and the ability to exercise control over the decision.<\/jats:p>","DOI":"10.1007\/s00146-023-01649-6","type":"journal-article","created":{"date-parts":[[2023,3,29]],"date-time":"2023-03-29T16:23:01Z","timestamp":1680106981000},"page":"1947-1960","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["The ABC of algorithmic aversion: not agent, but benefits and control determine the acceptance of automated decision-making"],"prefix":"10.1007","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4661-701X","authenticated-orcid":false,"given":"Gabi","family":"Schaap","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4233-0406","authenticated-orcid":false,"given":"Tibor","family":"Bosse","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9628-2476","authenticated-orcid":false,"given":"Paul","family":"Hendriks Vettehen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,3,29]]},"reference":[{"key":"1649_CR1","unstructured":"Araujo T, De Vreese C, Helberger N, Kruikemeier S, Van Weert J, Bol N, Oberski D, Pechenizkiy M, Schaap G, Taylor L (2018) Automated decision-making fairness in an AI-driven world: Public perceptions, hopes and concerns. 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