Expectations as selection constraints in human–AI communication: a Luhmannian account, with implications for AI safety

This record contains the preprint Expectations as selection constraints in human–AI communication: a Luhmannian account, with implications for AI safety. The paper develops a communication-level framework for analyzing how expectations shape the contributions selected in human–AI interaction. Drawing narrowly on Niklas Luhmann’s primary texts, it distinguishes program, role, value, and person as different ways of organizing expectations and introduces overdetermined and underdetermined configurations as tools for analyzing selection under competing or incomplete conditions. The framework is applied to four documented cases: evaluation agents discussed by METR, the July 2026 AISI cyber incident, the OpenAI–Hugging Face incident, and DAN role invocation. The paper is theoretical and interpretive: it offers a redescription and testable hypotheses about the relative effective valence of expectations organized through different identifications. It does not estimate prevalence, infer unobserved model states, or posit a fixed hierarchy among those identifications. Version 1.0 · Preprint