Quantifying Claim Robustness Through Adversarial Framing: A Conceptual Framework for an AI-Enabled Diagnostic Tool

Objectives: We introduce the conceptual framework for the Adversarial Claim Robustness Diagnostics (ACRD) protocol, a novel tool for assessing how factual claims withstand ideological distortion. Methods: Based on semantics, adversarial collaboration, and the devil’s advocate approach, we develop a three-phase evaluation process combining baseline evaluations, adversarial speaker reframing, and dynamic AI calibration along with quantified robustness scoring. We introduce the Claim Robustness Index that constitutes our final validity scoring measure. Results: We model the evaluation of claims by ideologically opposed groups as a strategic game with a Bayesian-Nash equilibrium to infer the normative behavior of evaluators after the reframing phase. The ACRD addresses shortcomings in traditional fact-checking approaches and employs large language models to simulate counterfactual attributions while mitigating potential biases. Conclusions: The framework’s ability to identify boundary conditions of persuasive validity across polarized groups can be tested across important societal and political debates ranging from climate change issues to trade policy discourses.