The Reconfiguration of Knowing in Locke's Account within the Age of AI

Abstrak

This study explores how advanced artificial intelligence may reshape traditional ideas about knowledge and about what it means to know. John Locke's philosophy provides a useful starting point because Locke presents knowledge as something grounded in human experience. In his account, we gain knowledge through sensory perception and through reflection on what we experience. These two elements, sensation and reflection, work together to form human understanding. As AI systems become more capable and increasingly able to imitate patterns of human reasoning, the question arises: do AI systems know in a way that resembles Locke's description, or do they challenge the boundaries that Locke's theory seems to assume? The paper follows an analytical approach to connect Locke's epistemology with key features of contemporary AI. It examines the extent to which AI systems can be compared to human knowing by asking what counts as “input,” what might correspond to “experience,” and whether machine processing can be meaningfully described as a form of “reflection.” AI systems often learn from large sets of data, identify regularities, and generate responses that may appear intelligent. Yet, Locke's account emphasizes that knowledge is not only about producing correct outputs; it also depends on the origin and justification of understanding in relation to experience and consciousness. This study therefore considers whether AI's method of learning can be interpreted as a kind of analogue to Locke's sensory basis, and whether AI's internal operations can be treated as analogous to reflective thought. In addition, the paper argues that the growing presence of AI blurs the line between human and machine in epistemic practices. People increasingly rely on AI tools for information, recommendation, and decision-making. This dependence raises concerns about the stability of “knowing” as a human-centered achievement. By combining Locke's framework with the realities of modern AI, the study concludes that traditional views of knowledge are challenged and potentially reconfigured. Rather than treating AI as simply a faster version of human reasoning, the paper suggests that AI forces us to reconsider the criteria of knowing itself, particularly the roles of experience, justification, and meaningful reflection.