The Human Knowledge Baseline: A Policy Framework for Protecting Scientific and Human Data from AI Pollution

This policy paper proposes the creation of a Human Knowledge Baseline (HKB): a provenance-controlled, human-authenticated reference corpus designed to preserve scientific, educational, cultural, historical, legal, agricultural, engineering, and institutional knowledge before synthetic information becomes inseparable from reality-derived records. Building upon the mathematical theory of AI pollution and recursive training contamination, the paper argues that modern generative artificial intelligence systems can transform uncertainty into persistent symbolic artefacts when unknown states are converted into plausible outputs and subsequently admitted into archives, repositories, citation systems, and future training datasets. The policy framework establishes that artificial intelligence should generate knowledge candidates, while human institutions should remain responsible for validation, authentication, provenance auditing, and admission into trusted knowledge systems. The paper further argues that AI-driven automation will create new categories of employment centred on evidence review, repository governance, provenance management, dataset stewardship, archival engineering, scientific authentication, and epistemic risk management. The Human Knowledge Baseline is presented as a constructive policy response designed to preserve humanity's external reference layer while allowing continued innovation in artificial intelligence.