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→User training: Reference added that shows how to reduce algorithm aversion through user training and learning effects |
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=== User training ===
Algorithmic recommendations represent a new type of information in many fields. For example, a medical AI diagnosis of a [[Infection|bacterial infection]] is different than a lab test indicating the presence of a bacteria. It has been shown that repeated tasks, constant feedback and financial incentives can contribute towards reducing algorithm aversion.<ref>{{Cite journal |last=Filiz |first=Ibrahim |last2=Judek |first2=Jan René |last3=Lorenz |first3=Marco |last4=Spiwoks |first4=Markus |date=2021-09-01 |title=Reducing algorithm aversion through experience |url=https://www.sciencedirect.com/science/article/pii/S221463502100068X |journal=Journal of Behavioral and Experimental Finance |language=en |volume=31 |pages=100524 |doi=10.1016/j.jbef.2021.100524 |issn=2214-6350}}</ref>
== Algorithm appreciation ==
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