The epistemic revolution of AI: reconfiguring the foundations of scientific knowledge

The integration of artificial intelligence (AI) into scientific practice represents not merely a methodological shift but a profound transformation in the epistemic structure of science. This article critically examines how AI disrupts classical epistemological paradigms—empiricism, falsificationism, Kuhnian paradigm shifts, and social epistemology—while necessitating novel frameworks for understanding knowledge production in the age of machine cognition. Through a systematic analysis of AI-mediated observation, theory generation, and distributed epistemic responsibility, this work reveals fundamental tensions between computational objectivity and human interpretative agency, data-driven induction and theory-laden observation, and algorithmic authority and democratic accountability. Case studies, such as AlphaFold’s predictive success and racial bias in medical AI, illustrate these tensions, demonstrating how AI both extends and challenges traditional norms of scientific justification. The present study proposes three constructive pathways forward: pragmatic computational empiricism, which balances predictive utility with normative safeguards; adversarial epistemology, fostering co-evolution between human and machine reasoning; and democratic AI epistemology, ensuring accountability in sociotechnical knowledge systems. Ultimately, this work argues that AI does not replace human epistemology but compels its reconfiguration, demanding new philosophical frameworks attuned to hybrid human–machine cognition.