Do artificial neural networks know?

Q: What does this research mean for the field?

Artificial neural networks are best understood as epistemic instruments whose outputs can carry knowledge only when embedded within appropriately regulated sociotechnical practices, rather than possessing independent propositional or implicit knowledge. Novelty: ClaimNovelty.INCREMENTAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

Lionel Augustin Thalmann

doi:10.25365/thesis.81411

July 7, 2026Open Access

Do artificial neural networks know?

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Key Points

The aim is to assess whether and how artificial neural networks can be attributed knowledge, using an epistemological perspective.
Employs an epistemological pluralism approach to analyze the competence of neural networks (CLERE)
Integrates concepts of propositional and implicit knowledge
Analyzes CLERE's performance and limitations through mathematical optimization and societal practices.
Propositional knowledge attribution is limited; CLERE relies on calibrated beliefs supported by evidence checks.
Implicit knowledge criteria, such as intentionality and empathy, are not met by CLERE's learning processes.
CLERE's outputs can bear knowledge but remain dependent on the sociotechnical practices that train and validate the system.

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Implication

Explores how artificial neural networks can be considered as sources of knowledge, suggesting their limitations and roles in society.

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Cite This Study

Lionel Augustin Thalmann (2026) studied this question.

synapsesocial.com/papers/6a4f39e32b81a944af574830 https://doi.org/10.25365/thesis.81411

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Do artificial neural networks know?

Key Points

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Implication

Abstract

Discussion

Cite This Study

Do artificial neural networks know?

Key Points

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Implication

Abstract

Discussion

Cite This Study

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