Hempel's D-N Model in a New Epistemological Era of AI

Abstract

Carl Gustav Hempel’s Deductive-Nomological (D-N) model emerged in the mid-20th century as a formal framework for scientific explanation, emphasizing deductive reasoning through general laws and initial conditions. However, the rise of Artificial Intelligence (AI) has transformed the landscape of scientific inquiry, challenging traditional explanatory models like the D-N model. AI systems, such as machine learning and neural networks, not only assist in processing vast data sets but also act as epistemic agents, raising questions about the nature of explanation and understanding. This essay critically examines the impact of AI on scientific explanation, exploring whether Hempel’s model remains relevant or requires revision. The opacity of AI processes, particularly in ‘black-box’ models, complicates the deductive clarity required by the D-N model, prioritizing prediction over traditional causal understanding. Furthermore, the rise of explainable AI (XAI) and causal inference underscores the growing need for transparency, interpretability, and pragmatic relevance in explanations. This shift from deductive to inductive reasoning suggests that while Hempel's D-N model remains influential, it must evolve to accommodate AI’s role in contemporary scientific discovery. The integration of AI demands a re-evaluation of what constitutes scientific explanation in the 21st century, balancing prediction, causality, and human-centred understanding.