Toward a Unified Epistemology of Human and Artificial Intelligence: Rethinking Knowledge Beyond the Human Mind Why this is cutting-edge

Background: As artificial intelligence becomes integral to knowledge creation; the nature of epistemic agency shifts. Generative models, autonomous discovery systems, and collaborative human–AI teams raise questions about how we define and justify knowledge in a mixed human–machine epistemic landscape.

Philosophical Problem: Classical epistemology, developed for individual human knowers, has no consensus answer to these developments. For instance, Freiman (2023) notes that AI-generated information defies standard categories of testimony. This suggests conceptual fragmentation: understanding, justification, and knowledge may all require rethinking when AI is a knowledge partner.

Literature Gap: While recent studies propose partial solutions—Samuel’s “epistemic co-agency” model, Punziano’s “adaptive epistemology”, and Durán & Formanek’s computational reliabilism, there is no unified framework tying these insights together. Existing epistemological theories remain largely anthropocentric or narrowly specialized, leaving the role of AI in knowledge systems under-theorized.

Research Objective: We critically examine whether a single Unified Epistemology of Human and AI is needed. This involves mapping how major epistemological concepts (knowledge, belief, justification, understanding, explanation, agency, reliability, testimony, collective cognition) have been reinterpreted (or not) in recent literature to accommodate AI. We ask: Do these efforts coherently integrate, or do gaps persist?

Conceptual Methodology: Through an interdisciplinary review and gap analysis, we identify unresolved tensions (e.g., whether AI can count as a believer, whether justification requires consciousness) and synthesize interdisciplinary insights. Drawing on philosophy of science (e.g., Hybrid norms for AI-driven discovery), philosophy of information, social epistemology, and cognitive science, we propose a tentative framework.

Expected Contribution: We outline a provisional unified epistemology that specifies how human and artificial agents jointly contribute to knowledge. Key features include emphasizing reliability with transparency, distributed justification across human–AI networks, and adaptive normative criteria that respect both human understanding and algorithmic inference. This model differs from existing theories by explicitly integrating AI-mediated processes rather than treating AI merely as tools or metaphors.

Significance for Philosophy: By proposing a common conceptual space, this work advances philosophical unity and interdisciplinary innovation, showing how diverse approaches (analytic normativity, computational modeling, virtue theory) can cohere in a new epistemology. It charts a path for the future of epistemology that responsibly incorporates computational methods, addressing the conference’s themes of unity, methodological diversity, and interdisciplinary inquiry. Our central claim is that the hybrid nature of modern knowledge systems calls for expanding epistemology beyond human-centric foundations; this claim is grounded in both the literature’s open problems and the identified need for new conceptual tools to account for AI’s epistemic role.

References:

1.      Freiman O. Analysis of Beliefs Acquired from a Conversational AI: Instruments-based Beliefs, Testimony-based Beliefs, and Technology-based Beliefs. Episteme. 2024;21(3):1031-1047. doi:10.1017/epi.2023.12

2.      Durán, J.M., Formanek, N. Grounds for Trust: Essential Epistemic Opacity and Computational Reliabilism. Minds & Machines 28, 645–666 (2018). https://doi.org/10.1007/s11023-018-9481-6

3.      Punziano, G. (2025). Adaptive Epistemology: Embracing Generative AI as a Paradigm Shift in Social Science. Societies, 15(7), 205. https://doi.org/10.3390/soc150702

4.      Clark, A., & Chalmers, D. (1998). The extended mind. analysis, 58(1), 7-19.

5.      Rivera-Novoa, A., Duarte Arias, D.A. Generative Artificial Intelligence and Extended Cognition in Science Learning Contexts. Sci & Educ 35, 755–776 (2026). https://doi.org/10.1007/s11191-025-00660-1

6.      Humphreys, P. (2004). Extending ourselves: Computational science, empiricism, and scientific method. Oxford University Press.

7.      Carter, J. A., Clark, A., Kallestrup, J., Palermos, S. O., & Pritchard, D. (Eds.). (2018). Extended epistemology. Oxford University Press.

8.      Nyrup R. The Limits of Value Transparency in Machine Learning. Philosophy of Science. 2022;89(5):1054-1064. doi:10.1017/psa.2022.61

9.      Lackey, J. (2008). Learning from words: Testimony as a source of knowledge. Oxford University Press.