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Social media platforms are increasingly deploying complex interventions tohelp users detect false news. Labeling false news using techniques that combinecrowd-sourcing with artificial intelligence (AI) offers a promising way toinform users about potentially low-quality information without censoringcontent, but also can be hard for users to understand. In this study, weexamine how users respond in their sharing intentions to information they areprovided about a hypothetical human-AI hybrid system. We ask i) if thesewarnings increase discernment in social media sharing intentions and ii) ifexplaining how the labeling system works can boost the effectiveness of thewarnings. To do so, we conduct a study ($N=1473$ Americans) in whichparticipants indicated their likelihood of sharing content. Participants wererandomly assigned to a control, a treatment where false content was labeled, ora treatment where the warning labels came with an explanation of how they weregenerated. We find clear evidence that both treatments increase sharingdiscernment, and directional evidence that explanations increase the warnings'effectiveness. Interestingly, we do not find that the explanations increaseself-reported trust in the warning labels, although we do find some evidencethat participants found the warnings with the explanations to be moreinformative. Together, these results have important implications for designingand deploying transparent misinformation warning labels, and AI-mediatedsystems more broadly.

Do explanations increase the effectiveness of AI-crowd generated fake news warnings?