Expectations as selection constraints in human–AI communication: a Luhmannian account, with implications for AI safety
Year: 2026
Type: preprint
Abstract: 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 1.1 — 29 August 2026. Updates the Sol–Hugging Face case using the 26 August 2026 METR/Redwood investigation and OpenAI postmortem; corrects the evidential basis for scope constraints, updates the documented incident scale, and adds the failed-scorer expectation and peer-generated authorization. The theoretical framework is unchanged. Version 1.0 · Preprint
Author: Haehner-Murdock, Christine
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7203908565
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