Connecting epistemic injustice and justified belief in health-related conspiracies
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Erosion of trust can cut both ways: marginalized groups may lend less credibility to dominant institutions, disadvantaging those institutions when they dispense knowledge for public good, such as medical advice.
“The erosion of trust can cut both ways: a marginalized group may lend less credibility to dominant institutions, which puts them at a disadvantage when these institutions try to dispense knowledge for the public good, such as medical advice (Annesley 2020).”
From Epistemic Injustice in Generative AI (2024) · cites this paper as Annesley 2020
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Epistemic Injustice in Generative AI
Kay, J., Kasirzadeh, A., & Mohamed, S. (2024) · Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1609/aies.v7i1.31671 · Cited by 76 · see citing works
- Authors
- Annesley, K.
- Affiliations
- University of Rochester (US)
- Corresponding author
- Annesley, K.
- ORCID
- Annesley, K.
- Venue
- Ethics, Medicine and Public Health
- Publisher
- Elsevier BV
- Published
- 2020-10
- Volume
- 15
- Pages
- 100545
- ISSN
- 2352-5525 (ISSN-L)
- OpenAlex
- W3082610887
- Cited by
- 3
- References
- 0
- Citations by year
- 2025: 1 · 2024: 1 · 2022: 1
- FWCI
- 0.33
- Type
- journal article
- Language
- fr
- Open access
- closed
- Locations
- Ethics Medicine and Public Health (publishedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Feminist Epistemology and Gender Studies
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Social Sciences › Arts and Humanities › Philosophy › Epistemology, Ethics, and Metaphysics
- Keywords
- epistemic injustice, testimonial injustice, hermeneutical injustice, women's health, health misinformation, social media, rational belief
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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