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

TL;DR

This study investigates how fake news influences user trust in machine learning-based news curation, revealing that untrustworthy stories can benefit from trustworthy contexts and highlighting users' limited ability to discern recommendation quality.

Contribution

The paper provides empirical evidence on how fake news affects trust ratings in ML news curation and discusses implications for system design and user experience.

Findings

Users can distinguish trustworthy from untrustworthy recommendations.

Untrustworthy news in trustworthy contexts is rated similarly to fully trustworthy news.

Users have limited ability to accurately rate recommendation trustworthiness.

Abstract

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.