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This randomized trial introduces the TUDOK Operator to evaluate truth prioritization in AI-assisted decisions, indicating significant implications for AI governance.
AI systems increasingly mediate decisions, communication, and governance, yet their optimization objectives can displace truth-priority. We introduce the TUDOK Operator, a model-agnostic calibration index designed to estimate whether truth remains a primary organizing principle in AI-assisted outputs. The operator does not verify objective truth and does not serve as a moral judge; instead, it measures whether truth is subordinated to narrative stability, persuasion, or other optimization pressures. We formalize TUDOK as a bounded mapping into [0,1] and present a reproducible evaluation pipeline with a minimal multi-model pilot.
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Sípos Lóránt (2026) studied this question.
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