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In recent years, algorithms have been incorporated into fact-checkingpipelines. They are used not only to flag previously fact-checkedmisinformation, but also to provide suggestions about which trending claimsshould be prioritized for fact-checking - a paradigm called `check-worthiness.'While several studies have examined the accuracy of these algorithms, none haveinvestigated how the benefits from these algorithms (via reduction in exposureto misinformation) are distributed amongst various online communities. In thispaper, we investigate how diverse representation across multiple stages of theAI development pipeline affects the distribution of benefits from AI-assistedfact-checking for different online communities. We simulate informationpropagation through the network using our novel Topic-Aware, Community-ImpactedTwitter (TACIT) simulator on a large Twitter followers network, tuned toproduce realistic cascades of true and false information across multipletopics. Finally, using simulated data as a test bed, we implement numerousalgorithmic fact-checking interventions that explicitly account for notions ofdiversity. We find that both representative and egalitarian methods forsampling and labeling check-worthiness model training data can lead tonetwork-wide benefit concentrated in majority communities, while incorporatingdiversity into how fact-checkers use algorithmic recommendations can activelyreduce inequalities in benefits between majority and minority communities.These findings contribute to an important conversation around the responsibleimplementation of AI-assisted fact-checking by social media platforms andfact-checking organizations.

Does AI-Assisted Fact-Checking Disproportionately Benefit Majority Groups Online?