From Knowledge-Centric Computation to Control-Centric Intelligence: Rethinking AI Systems through the DIKCA Framework

Contemporary artificial intelligence systems, particularly large language models (LLMs), are increasingly governed not only by what they know, but by how their behavior is constrained, steered, and revised. Current discourse frequently conflates these governing mechanisms with knowledge itself, leading to conceptual confusion and weakened accountability. This paper argues that such conflation constitutes a fundamental category error. To address this, we propose DIKCA-an extension of the classical DIKW hierarchy that explicitly introduces Control and Autonomy as distinct computational and socio-technical layers. In DIKCA, control specifies permissible and prioritized actions, while autonomy denotes a system's capacity to modify its own control structures over time. We argue that intelligence, in this framework, is best understood as self-modifying control rather than the accumulation of knowledge. By separating epistemic capacity from behavioral governance, DIKCA provides a principled basis for algorithmic accountability, legal responsibility, and democratic oversight. The framework clarifies where failures occur-whether in data, knowledge, control, or autonomy-and enables governance mechanisms that are both technically grounded and socially contestable.