text: 337,11,1521291232080760833,278791721,Maria De-Arteaga,"📣 New #facct2022 paper “Justice in Misinformation Detection Systems: An Analysis of Algorithms, Stakeholders, and Potential Harms”. Led by 1st year @UTexasMcCombs PhD student @TerrenceNeumann (say hi to him in Seoul!), and joint with @sinafazelpur 🧵 <LINK> <LINK> As misinformation detection pipelines increasingly incorporate algorithms, justice of these systems become a central concern. Misinformation detection pipelines consist of varied tasks, each of which can give rise to many ethical concerns, and involve multiple stakeholders. 2/5 <LINK> Commonly, algorithmic fairness considers cases where each data instance pertains a single direct stakeholder that occupies one role: decision subject. In the case of informational items, in contrast, multiple stakeholders are directly implicated in each informational item. 3/5 <LINK> We employ and extend upon the notion of informational justice to develop a framework for explicating issues of justice relating to misinformation detection systems, considering representation, participation, allocation, and credibility affecting different stakeholders. 4/5 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. 5/5",https://arxiv.org/abs/2204.13568,"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. ","Justice in Misinformation Detection Systems: An Analysis of Algorithms,