The Human Reference Collapse: A New AI Risk Framework for Measuring the Loss of Original Human Ground Truth in Synthetic Societies HRC

Human Reference Collapse (HRC) introduces a new AI risk framework for understanding what happens when the original human ground truth layer begins to weaken under large-scale AI mediation. While current AI safety research focuses on model collapse, synthetic data, deepfakes, bias, hallucination, and governance, this paper argues that a deeper risk is emerging: AI systems may gradually lose access to independent human reality because humans themselves are increasingly using AI to write, think, decide, learn, create, communicate, and verify information. The paper defines the Human Reference Layer as the foundation of original human knowledge, judgment, creativity, lived experience, social trust, and feedback that AI systems depend on for training, evaluation, and alignment. It then explains how this layer can degrade through synthetic saturation, AI-mediated behavior, provenance failure, feedback contamination, cultural compression, and institutional outsourcing of judgment. The central thesis is clear: Model collapse is the machine-side symptom. Human Reference Collapse is the civilization-side cause. This paper proposes the Human Reference Collapse Index (HRCI) as an early measurement framework for assessing the loss of original human ground truth across education, journalism, scientific publishing, search, governance, business, law, and public knowledge systems. HRC is a foundational paper for researchers, policymakers, AI governance professionals, educators, publishers, and institutions concerned with preserving human authenticity, knowledge integrity, and reality anchoring in the age of artificial intelligence.

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