Artificial Intelligence and Lokean Epistemology

Abstract

Abstract— This research explores the intersection of artificial intelligence (AI) and John Locke’s epistemology, examining how advancements in AI challenge traditional notions of knowledge and the subject of knowledge. The increasing sophistication of AI systems, which simulate human-like reasoning and learning processes, blurs the boundaries between human cognition and machine intelligence. This study investigates the potential connections between AI and Locke's theory of knowledge, which emphasizes that knowledge arises from sensory experience and reflection. Beginning with a review of Locke’s epistemological principles, including the role of empirical data and the distinction between primary and secondary qualities, the research evaluates how AI’s reliance on vast datasets, machine learning algorithms, and neural networks aligns—or diverges—from Locke’s framework. It questions whether AI systems can possess knowledge in the Lockean sense and examines the epistemic status of AI-generated outputs in terms of reliability, trustworthiness, and biases in training data. The role of human oversight in validating AI-generated insights is also critically assessed. Ultimately, this study contributes to the ongoing discourse on the nature and limits of knowledge in the AI era, challenging traditional epistemological frameworks. By integrating Locke’s principles with contemporary AI developments, it advances the debate on what it means to "know" in a world increasingly mediated by artificial agents, offering a nuanced perspective on the implications of AI for human understanding and the evolving landscape of knowledge.