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Algorithm aversion has been studied in a wide variety of contexts. For example, people seem to prefer recommendations for jokes from a human rather than from an algorithm,<ref name=":2">{{Cite journal|last=Yeomans|first=Michael|last2=Shah|first2=Anuj|last3=Mullainathan|first3=Sendhil|last4=Kleinberg|first4=Jon|date=2019|title=Making sense of recommendations|url=https://onlinelibrary.wiley.com/doi/abs/10.1002/bdm.2118|journal=Journal of Behavioral Decision Making|language=en|volume=32|issue=4|pages=403–414|doi=10.1002/bdm.2118|issn=1099-0771}}</ref> and would rather rely on a human to predict the number of airline passengers from each US state instead of an algorithm.<ref>{{Cite journal|last=Dietvorst|first=Berkeley J.|last2=Simmons|first2=Joseph P.|last3=Massey|first3=Cade|date=2015|title=Algorithm aversion: People erroneously avoid algorithms after seeing them err.|url=http://doi.apa.org/getdoi.cfm?doi=10.1037/xge0000033|journal=Journal of Experimental Psychology: General|language=en|volume=144|issue=1|pages=114–126|doi=10.1037/xge0000033|issn=1939-2222}}</ref> People also seem to prefer medical recommendations from human doctors instead of an algorithm.{{Citation needed|date=September 2021}}
== Proposed
Various frameworks have been proposed to explain the causes for algorithm aversion and techniques or system features that might help reduce aversion.<ref name=":0" /><ref>{{Cite journal|last=Burton|first=Jason W.|last2=Stein|first2=Mari-Klara|last3=Jensen|first3=Tina Blegind|date=2020|title=A systematic review of algorithm aversion in augmented decision making|url=https://onlinelibrary.wiley.com/doi/abs/10.1002/bdm.2155|journal=Journal of Behavioral Decision Making|language=en|volume=33|issue=2|pages=220–239|doi=10.1002/bdm.2155|issn=1099-0771}}</ref>
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