  In recent years, algorithms have been incorporated into fact-checking
pipelines. They are used not only to flag previously fact-checked
misinformation, but also to provide suggestions about which trending claims
should be prioritized for fact-checking - a paradigm called `check-worthiness.'
While several studies have examined the accuracy of these algorithms, none have
investigated how the benefits from these algorithms (via reduction in exposure
to misinformation) are distributed amongst various online communities. In this
paper, we investigate how diverse representation across multiple stages of the
AI development pipeline affects the distribution of benefits from AI-assisted
fact-checking for different online communities. We simulate information
propagation through the network using our novel Topic-Aware, Community-Impacted
Twitter (TACIT) simulator on a large Twitter followers network, tuned to
produce realistic cascades of true and false information across multiple
topics. Finally, using simulated data as a test bed, we implement numerous
algorithmic fact-checking interventions that explicitly account for notions of
diversity. We find that both representative and egalitarian methods for
sampling and labeling check-worthiness model training data can lead to
network-wide benefit concentrated in majority communities, while incorporating
diversity into how fact-checkers use algorithmic recommendations can actively
reduce inequalities in benefits between majority and minority communities.
These findings contribute to an important conversation around the responsible
implementation of AI-assisted fact-checking by social media platforms and
fact-checking organizations.
