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

被引：4
|
作者：
Heuer, Hendrik [1 ,2 ]
Breiter, Andreas [1 ,2 ]
机构：
[1] Inst Informat Management Bremen GmbH Ifib, Bremen, Germany
[2] Univ Bremen, Ctr Media Commun & Informat Res ZeMKI, Bremen, Germany
来源：
DISINFORMATION IN OPEN ONLINE MEDIA, MISDOOM 2020 | 2020年 / 12259卷
关键词：
Human-centered machine learning; Algorithmic experience; Algorithmic bias; Fake news; Social media; AUTOMATION; PEOPLE;
D O I：
10.1007/978-3-030-61841-4_2
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
引用
收藏
页码：18 / 36
页数：19
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