Artificial Knowledge and its Challenges for Knowledge Management Systems

It is a paradox that literature exploded in the last years with papers on “artificial intelligence”, but only a few of them dealt with “artificial knowledge”. The purpose of this paper is to analyze the concept of “artificial knowledge” and to compare it with human knowledge. That helps us to bridge the gap between artificial knowledge and knowledge management. More specifically, we focus on Generative Artificial Intelligence (GenAI), which has the capacity to simulate a human-like dialogue using natural language. The relevance of the present research comes from the need to understand the similarities and dissimilarities between artificial knowledge and human knowledge, and how both of them can be integrated into knowledge management systems. It is a conceptual paper based on a systematic literature review and a critical thinking approach. We used the theory of knowledge fields and knowledge dynamics for human knowledge and a GenAI analysis for artificial knowledge. Findings show that ChatGPT can answer a human interrogator by formulating texts comparable to those written by people. However, the imitation of human knowledge is not in its nature but in its functionality. The generation of artificial knowledge is completely different than that of human knowledge. Human knowledge is created by our sensory system and the brain through several processes, like perception and reflection. Artificial knowledge is generated by GenAI based on syntactic rules without any correlation with semantics. Computers cannot think and have no cognitive processes. Knowledge managers should be able to integrate both artificial and human knowledge into knowledge management systems. The main contribution of this paper is that it analyzes artificial knowledge and its generation from a knowledge management perspective. The main limitation of the present research comes from the scarcity of papers on the subject of artificial knowledge.