论文标题
假新闻如何影响新闻策划机器学习系统输出的信任
How Fake News Affect Trust in the Output of a Machine Learning System for News Curation
论文作者
论文摘要
人们越来越多地消费通过机器学习（ML）系统策划的新闻。在研究算法偏见的研究中，本文探讨了算法新闻策展系统用户信任的建议以及该信任如何受到虚假新闻等不可信的新闻故事的影响。在与82名具有背景的职业学校学生的研究中，我们发现用户能够提供信任评级，从而将优质新闻故事的值得信赖的建议与不值得信赖的建议区分开来。但是，一个不值得信赖的新闻故事与四个值得信赖的新闻故事相似，被评为五个值得信赖的新闻报道。结果可能是第一个迹象表明，不值得信赖的新闻报道受益于在值得信赖的背景下出现。结果还显示了用户能力评估新闻策展系统建议的局限性。我们讨论了这对交互式机器学习系统的用户体验的含义。
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.
