  Faced with the scale and surge of misinformation on social media, many
platforms and fact-checking organizations have turned to algorithms for
automating key parts of misinformation detection pipelines. While offering a
promising solution to the challenge of scale, the ethical and societal risks
associated with algorithmic misinformation detection are not well-understood.
In this paper, we employ and extend upon the notion of informational justice to
develop a framework for explicating issues of justice relating to
representation, participation, distribution of benefits and burdens, and
credibility in the misinformation detection pipeline. Drawing on the framework:
(1) we show how injustices materialize for stakeholders across three
algorithmic stages in the pipeline; (2) we suggest empirical measures for
assessing these injustices; and (3) we identify potential sources of these
harms. This framework should help researchers, policymakers, and practitioners
reason about potential harms or risks associated with these algorithms and
provide conceptual guidance for the design of algorithmic fairness audits in
this domain.
