Istisarelism 4.0: Proportionate Objection, Epistemic Autonomy and Reasoned Termination in Human–AI Deliberation

This preprint presents Istisarelism 4.0 as a conceptual and methodological framework for evidence-sensitive human–AI deliberation. The study retains seven foundational principles and four methodological components, focusing on proportionate objection, epistemic autonomy, justified revision, reciprocal error detection, and reasoned termination. Version 4.0 extends the framework by examining the distinction between evidence-based persuasion and sycophantic agreement. Three documented human-mediated Google AI interaction cases (LAB-002 M, N and O) provide limited qualitative observations concerning unsupported inferences, user objections, self-correction and epistemic accountability. The paper also introduces nine candidate evaluation mechanisms associated with the ÖNSEZİ early-warning research program and proposes falsifiable research questions for future empirical investigation. The framework is presented as a theoretical proposal rather than an experimentally validated AI architecture. The observational cases do not establish persistent model learning, internal motivations or empirical superiority. This work extends the previously published Istisarelism 3.0 preprint (DOI: 10.5281/zenodo.23839677). Version 2.0 is retained as an unpublished historical draft. Author: Ali DeğişmişVersion: 4.0Publication type: PreprintDOI: 10.5281/zenodo.23886026