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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. When decision makers are faced with a task for the first time, they may be especially hesitant to use an algorithm. It has been shown that learning effects achieved through 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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