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People are increasingly consuming news curated by machine learning (ML)systems. Motivated by studies on algorithmic bias, this paper explores whichrecommendations of an algorithmic news curation system users trust and how thistrust is affected by untrustworthy news stories like fake news. In a study with82 vocational school students with a background in IT, we found that users areable to provide trust ratings that distinguish trustworthy recommendations ofquality news stories from untrustworthy recommendations. However, a singleuntrustworthy news story combined with four trustworthy news stories is ratedsimilarly as five trustworthy news stories. The results could be a firstindication that untrustworthy news stories benefit from appearing in atrustworthy context. The results also show the limitations of users' abilitiesto rate the recommendations of a news curation system. We discuss theimplications of this for the user experience of interactive machine learningsystems.

How Fake News Affect Trust in the Output of a Machine Learning System for News Curation