  The massive volume of online information along with the issue of
misinformation has spurred active research in the automation of fact-checking.
Like fact-checking by human experts, it is not enough for an automated
fact-checker to just be accurate, but also be able to inform and convince the
user of the validity of its predictions. This becomes viable with explainable
artificial intelligence (XAI). In this work, we conduct a study of XAI
fact-checkers involving 180 participants to determine how users' actions
towards news and their attitudes towards explanations are affected by the XAI.
Our results suggest that XAI has limited effects on users' agreement with the
veracity prediction of the automated fact-checker and on their intent to share
news. However, XAI nudges users towards forming uniform judgments of news
veracity, thereby signaling their reliance on the explanations. We also found
polarizing preferences towards XAI and raise several design considerations on
them.
