nohEL

NohEL is a hypothetical framework used in discussions of artificial intelligence and governance to explore how autonomous systems might justify their actions through a formal, machine-readable explanation language. In speculative usage, nohEL expands the acronym no-human-in-the-loop Explanation Language, indicating a design where agents generate auditable justifications independent of real-time human input. The concept emphasizes external verifiability, traceability of decisions, and compliance with predefined safety and ethical constraints.

Core ideas attributed to nohEL include a structured, composable vocabulary for intents, outcomes, and rationales; formal semantics that allow automated auditing; and a separation between decision policies and the explanation layer. Proponents argue that such a language could facilitate accountability, red-teaming of policies, and cross-system interoperability for regulatory review. Critics note that a pure no-human-in-the-loop approach may increase exposure to incorrect or manipulated explanations, and that creating universally trustworthy explanation languages is a substantial technical and legal challenge. The term is mainly encountered in theoretical papers, forums, and hypothetical scenarios rather than as an established standard or widely deployed framework.

See also: Explainable AI, AI governance, human-in-the-loop, transparency in AI.