Artificial Intelligence and the Problem of Induction: A Critique of David Hume's Empiricism

Abstract

This paper critically examines the implications of Hume’s empiricism for contemporary AI, focusing on machine learning, decision trees, probabilistic models, and deep learning architectures. Through philosophical analysis and applied illustrations from healthcare, finance, marketing, and pattern recognition, the study shows how AI systems operationalize induction through hypothesis generation, pattern recognition, Bayesian updating, and hierarchical neural representations, while implicitly presupposing the very regularities Hume questioned. Although these systems achieve remarkable practical success, their inferences remain epistemically fallible and vulnerable to distributional shifts and uncertainty. The paper further critiques Hume’s strict empiricism by demonstrating that modern AI depends not solely on sensory data but also on prior assumptions, probabilistic reasoning, mathematical structure, and model-based constraints that exceed a purely experiential account of knowledge. While Bayesian and hybrid approaches partially mitigate inductive uncertainty by embedding probabilistic humility and corrigibility, they do not eliminate the philosophical problem itself. The study concludes that AI neither refutes nor resolves Hume’s problem of induction but reframes it, shifting emphasis from logical certainty to probabilistic adequacy, reliability, and ethical responsibility in the design and deployment of intelligent systems.

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