Machine Learning Explanations to Prevent Overtrust in Fake News Detection
Keywords:Qualitative and quantitative studies of social media, Human computer interaction; social media tools; navigation and visualization, Credibility of online content, Trust; reputation; recommendation systems
AbstractCombating fake news and misinformation propagation is a challenging task in the post-truth era. News feed and search algorithms could potentially lead to unintentional large-scale propagation of false and fabricated information with users being exposed to algorithmically selected false content. Our research investigates the effects of an Explainable AI assistant embedded in news review platforms for combating the propagation of fake news. We design a news reviewing and sharing interface, create a dataset of news stories, and train four interpretable fake news detection algorithms to study the effects of algorithmic transparency on end-users. We present evaluation results and analysis from multiple controlled crowdsourced studies. For a deeper understanding of Explainable AI systems, we discuss interactions between user engagement, mental model, trust, and performance measures in the process of explaining. The study results indicate that explanations helped participants to build appropriate mental models of the intelligent assistants in different conditions and adjust their trust according to their perceptions of model limitations.
How to Cite
Mohseni, S., Yang, F., Pentyala, S., Du, M., Liu, Y., Lupfer, N., Hu, X., Ji, S., & Ragan, E. (2021). Machine Learning Explanations to Prevent Overtrust in Fake News Detection. Proceedings of the International AAAI Conference on Web and Social Media, 15(1), 421-431. Retrieved from https://ojs.aaai.org/index.php/ICWSM/article/view/18072