K. Yee, Adrian (2023) Information Deprivation and Democratic Engagement. [Preprint]

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Abstract

There remains no consensus among social scientists as to how to measure and understand forms of information deprivation such as misinformation. Machine learning and statistical analyses of information deprivation typically contain problematic operationalizations which are too often biased towards epistemic elites’ conceptions that can undermine their empirical adequacy. A mature science of information deprivation should include considerable citizen involvement that is sensitive to the value-ladenness of information quality and that doing so may improve the predictive and explanatory power of extant models.

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Item Type:	Preprint
Creators:	Creators Email ORCID K. Yee, Adrian adriankyle.yee@mail.utoronto.ca
Keywords:	social epistemology, machine learning, information sciences, misinformation, models, construct validity, political philosophy, epistemology, science and values, democratic theory, measurement, operationalization
Subjects:	General Issues > Evidence Specific Sciences > Artificial Intelligence > Machine Learning General Issues > Models and Idealization General Issues > Science and Policy General Issues > Social Epistemology of Science General Issues > Values In Science
Depositing User:	Adrian K. Yee
Date Deposited:	08 Mar 2023 14:33
Last Modified:	08 Mar 2023 14:33
Item ID:	21852
Subjects:	General Issues > Evidence Specific Sciences > Artificial Intelligence > Machine Learning General Issues > Models and Idealization General Issues > Science and Policy General Issues > Social Epistemology of Science General Issues > Values In Science
Date:	2023
URI:	https://philsci-archive.pitt.edu/id/eprint/21852

Available Versions of this Item

• Measuring Information Deprivation: A Democratic Proposal. (deposited 22 Jul 2022 22:33)
  • Information Deprivation and Democratic Engagement. (deposited 08 Mar 2023 14:33) [Currently Displayed]

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