The Epistemology of AI – Redefining Knowledge in the Age of Generative Systems

Mini Track Chair: Dr. Mitt Nowshade Kabir, Trouvus.com – An AI consulting company.

Generative AI systems now routinely produce text, images, code, models, and even scientific hypotheses at scale. As these systems move from tools of assistance to active participants in knowledge production, they raise foundational questions that cut across philosophy, science, education, and institutional practice. This mini track invites contributions that interrogate the epistemological consequences of artificial intelligence: not merely how AI is used, but how its generative, probabilistic, and opaque modes of operation challenge established notions of knowledge, understanding, explanation, justification, authorship, and truth. We seek interdisciplinary work that advances conceptual clarity, empirical insight, and normative reflection on how AI systems reshape the conditions under which knowledge is created, validated, trusted, and governed. Submissions may be theoretical, empirical, design-oriented, or hybrid in nature.

• Foundations of Knowledge in the Age of AI How do large language models and generative systems challenge classical epistemic categories such as belief, justification, explanation, and truth? What does “understanding” mean when knowledge is produced by systems that do not possess semantic or intentional states? Can machine-generated outputs count as knowledge, and if so, for whom and under what conditions?
• AI in Science, Research, and Academia Epistemic roles of AI in hypothesis generation, theory formation, literature synthesis, and peer review AI-assisted research workflows: trust, verification, reproducibility, and error propagation Authorship, credit, and accountability in human–AI co-produced scholarship · Experimental and Applied Epistemology
• Empirical studies of how individuals and institutions evaluate AI-generated knowledge claims Human–AI epistemic collaboration: interfaces, workflows, and decision-support systems Designing AI systems that are epistemically transparent, accountable, or uncertainty-aware ·
• Epistemic Risks, Hallucinations, and Uncertainty Hallucinations, plausible falsehoods, and epistemic fragility in generative models Institutional and organizational responses to epistemic uncertainty introduced by AI Governance strategies for mitigating epistemic harm in synthetic text, media, and data
• Philosophical and Theoretical Perspectives AI and the philosophy of science, language, mind, and social epistemology Post-human, distributed, or synthetic epistemologies