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This research analyzes risks in misinformation detection using machine learning, indicating implications for diverse societies.
Machine learning models of misinformation detection are increasingly being used and yet their risks have been under analyzed, requiring insights from philosophy of science and political philosophy. A taxonomy of types of risks and simple models estimating them is provided that is sensitive to the value-laden features of judgments of misinformation and weighted by a potential item of misinformation’s impact on respective stakeholders. Failing to do so is incompatible with a variety of civil virtues that are desired by not only liberal democratic societies but authoritarian ones as well, suggesting the general applicability of this taxonomy of risks to contemporary and future societies.
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Adrian K. Yee (2026) studied this question.
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