Development of a Checklist on KI-Generated Texts

Automated approaches to evaluating AI-generated texts face a structural paradox: the more authoritative an automated system appears, the greater the risk that it induces unjustified confidence in human users. This article presents SCHNELLCHECK ULTRA v1.6.2, a descriptive AI auditing framework explicitly designed to avoid truth judgments, correctness classifications, and approval signals. Building on iterative adversarial red-teaming, the revised framework introduces three governance mechanisms—qualified user accountability, adversarial perspective sourcing, and enforced interpretive finalization—to address sociotechnical risks such as institutional bias, expert ambiguity, and procedural ritualization. Rather than acting as a validator, the system positions AI as a source of structured epistemic friction, preserving human responsibility while making uncertainty, omission, and perspective divergence explicit. We argue that robust AI governance does not emerge from stronger automated judgment, but from the controlled limitation of machine authority combined with auditable human accountability.