How Fake News Affect Trust in the Output of a Machine Learning System for News Curation
This addresses the problem of user trust in algorithmic news curation for consumers, highlighting incremental insights into how fake news can undermine system credibility.
The paper investigated how users trust recommendations from a machine learning news curation system and the impact of fake news on that trust, finding that users can distinguish trustworthy from untrustworthy recommendations but a single fake news story in a trustworthy context is rated similarly to all-trustworthy content.
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.