Entropy-Bounded Data Pipelines and Entropy-Frugal Intelligence: A Compositional Framework for Quantitative Assurance in Industrial AI Systems

Safety-critical AI deployments increasingly rely on deep, heterogeneous data pipelines and learned models executing across distributed infrastructure. Today these systems lack quantitative, compositional guarantees on how uncertainty, information loss, and model-induced fabrication accumulate from raw inputs to decisions. This paper proposes a unified information-theoretic framework—**Entropy-Bounded Data Pipelines (EBDP)** and **Entropy-Frugal Intelligence (EFI)**—that endows industrial AI systems with mathematically checkable contracts.