Information Deprivation and Democratic Engagement

Today's post is by Adrian K. Yee. Adrian is finishing his PhD at the University of Toronto focusing on the intersection of philosophy of science, politics, and economics (PPE) and will begin a position as Research Assistant Professor at Lingnan University starting August 2023.

Adrian has previously published on ‘econophysics’ (applications of physics to economic & financial phenomena) and misinformation studies, and has a forthcoming paper improving the methodology of Universal Basic Income studies. His upcoming research projects focus on philosophy of AI, attention economics, and epistemological & ethical issues in military intelligence analysis.

Adrian K. Yee

In the paper ‘Information Deprivation and Democratic Engagement’, I argue that 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.

There are three central problems extant models face. Firstly, operationalizations of misinformation are too focused on problematic alethic accounts (i.e. misinformation defined as simply false information) considering there is false information that is extremely useful and explanatorily powerful (e.g. any regression method in applied statistics) and there is true information that is misinformation (e.g. spin, malinformation, misleadingness etc.).

Secondly, the intrinsic value-ladenness of judgments of misinformation entails that determining information quality is not something intersubjectively verifiable in the way natural scientific phenomena are; hence, information quality is intrinsically socially constructed and negotiable by relevant stakeholders.

Thirdly, it follows that if we wish to avoid epistocracies, or harmful misinformation laws like Singapore’s since 2019, which fail to provide an adequate definition of misinformation (i.e. it simply allows the state to fine or imprison those it considers engaged in seditious informational practices), then we require further democratic engagement in the adjudication of information quality. As it stands, epistemic elites (e.g. journalists, university researchers, government policymakers, etc.) are almost entirely the ones who are solicited for adjudicating information quality, especially in supervised learning contexts in algorithmic content curation.

And yet, given that information quality is manifestly not merely a matter of truth, but can involve considerations of explanatory power, relevance, aesthetic quality, moral consequences, parsimony, and other epistemic and non-epistemic values, it follows that such values will require input from average citizens much more so than is currently the case in extant models.

Delusions in the DSM 5

This post is by Lisa Bortolotti. How has the definition of delusions changed in the DSM 5? Here are some first impressions. In the DSM-IV (Glossary) delusions were defined as follows: Delusion. A false belief based on incorrect inference about external reality that is firmly sustained despite what almost everyone else believes and despite what constitutes incontrovertible and obvious proof or evidence to the contrary. The belief is not one ordinarily accepted by other members of the person's culture or subculture (e.g., it is not an article of religious faith). When a false belief involves a value judgment, it is regarded as a delusion only when the judgment is so extreme as to defy credibility.

Rationalization: Why your intelligence, vigilance and expertise probably don't protect you

Today's post is by Jonathan Ellis , Associate Professor of Philosophy and Director of the Center for Public Philosophy at the University of California, Santa Cruz, and Eric Schwitzgebel , Professor of Philosophy at the University of California, Riverside. This is the first in a two-part contribution on their paper "Rationalization in Moral and Philosophical thought" in Moral Inferences , eds. J. F. Bonnefon and B. Trémolière (Psychology Press, 2017). We’ve all been there. You’re arguing with someone – about politics, or a policy at work, or about whose turn it is to do the dishes – and they keep finding all kinds of self-serving justifications for their view. When one of their arguments is defeated, rather than rethinking their position they just leap to another argument, then maybe another. They’re rationalizing –coming up with convenient defenses for what they want to believe, rather than responding even-handedly to the points you're making. Yo

A co-citation analysis of cross-disciplinarity in the empirically-informed philosophy of mind

Today's post is by Karen Yan (National Yang Ming Chiao Tung University) on her recent paper (co-authored with Chuan-Ya Liao), " A co-citation analysis of cross-disciplinarity in the empirically-informed philosophy of mind " ( Synthese 2023). Karen Yan What drives us to write this paper is our curiosity about what it means when philosophers of mind claim their works are informed by empirical evidence and how to assess this quality of empirically-informedness. Building on Knobe’s (2015) quantitative metaphilosophical analyses of empirically-informed philosophy of mind (EIPM), we investigated further how empirically-informed philosophers rely on empirical research and what metaphilosophical lessons to draw from our empirical results. We utilize scientometric tools and categorization analysis to provide an empirically reliable description of EIPM. Our methodological novelty lies in integrating the co-citation analysis tool with the conceptual resources from the philosoph

Imperfect Cognitions

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