AI and the Philosophy of Expertise and Epistemic Authority
Abstract
This chapter argues that AI systems will increasingly become part of our division of epistemic labor, fulfilling purposes and tasks previously reserved for human experts and epistemic authorities. This poses new challenges for both our epistemic practice and socio-epistemological theorizing. The chapter analyzes these challenges by relating them to key problems that have been discussed in the epistemology of expertise and epistemic authority: the definition problem, the identification problem, the deference problem, and the transfer problem.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Trust is an overarching theme that arises in all previously discussed topics and the development and deployment of AI generally.
“Trust is an overarching theme that arises in all previously discussed topics and the development and deployment of AI generally [5, 138, 139, 145, 146].”
From Autonomous artificial intelligence, scientific research, and human values (2026) · cites this paper as [139]
The citation lists haven’t been fetched yet
References 80 · Cited by 5
- Authors
- Hauswald, R.
- Corresponding author
- Hauswald, Rico
- Venue
- A Companion to Applied Philosophy of AI
- Publisher
- Wiley
- Published
- 2025-06-23
- Pages
- 55-70
- OpenAlex
- W4411566797
- Cited by
- 5
- References
- 80
- Citations by year
- 2026: 3 · 2025: 2
- Type
- other
- Language
- en
- Open access
- closed
- Topics
- Social Sciences › Arts and Humanities › Philosophy › Epistemology, Ethics, and Metaphysics
- Social Sciences › Sociology and Political Science › Feminist Epistemology and Gender Studies
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- artificial intelligence, epistemic authority, expertise, deference, identification problem, transfer problem, socioepistemology
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
