PROBLEMS WITH AI TRAINING: SOURCES AND CONSEQUENCES OF HALLUCINATION IN NEURAL NETWORK MODELS
This paper investigates the structural and methodological sources of hallucination in neural network models, examines three predominant training paradigms, and argues that incomplete training data, flawed train–test partitioning, and the inaccessibility of expert tacit knowledge are root causes of unreliable AI outputs...