Justice in Misinformation Detection Systems: An Analysis of Algorithms, Stakeholders, and Potential Harms

Abstract

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.

arXiv commentAccepted at ACM Conference on Fairness, Accountability, and Transparenct (FAccT), 2022

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Cited by in corpus (4)

• The State of Human-centered NLP Technology for Fact-checking
• Secondary Stakeholders in AI: Fighting for, Brokering, and Navigating Agency
• Exploring Multidimensional Checkworthiness: Designing AI-assisted Claim Prioritization for Human Fact-checkers
• Disagreement as a way to study misinformation and its effects