  People are increasingly consuming news curated by machine learning (ML)
systems. Motivated by studies on algorithmic bias, this paper explores which
recommendations of an algorithmic news curation system users trust and how this
trust is affected by untrustworthy news stories like fake news. In a study with
82 vocational school students with a background in IT, we found that users are
able to provide trust ratings that distinguish trustworthy recommendations of
quality news stories from untrustworthy recommendations. However, a single
untrustworthy news story combined with four trustworthy news stories is rated
similarly as five trustworthy news stories. The results could be a first
indication that untrustworthy news stories benefit from appearing in a
trustworthy context. The results also show the limitations of users' abilities
to rate the recommendations of a news curation system. We discuss the
implications of this for the user experience of interactive machine learning
systems.
