Quantifying Claim Robustness Through Adversarial Framing: An AI-Enabled Diagnostic Tool

This article introduces the Adversarial Claim Robustness Diagnostics (ACRD) protocol, a novel conceptual framework for assessing how factual claims withstand ideological distortion. Building on Tarski's (1944) semantic theory, contemporary work in cultural cognition (Kahan, 2017), adversarial collaboration (Ceci et al., 2024) and the Devil’s Advocate Approach (Vrij et al., 2023), we develop a three-phase evaluation process combining baseline evaluations, adversarial speaker reframing, dynamic calibration, and quantified robustness scoring. We model the evaluation of claims by ideologically opposed groups as a strategic game with a Bayesian Nash equilibrium, to infer what the possible behavior of evaluators might be after the adversarial collaboration phase. The ACRD addresses shortcomings in traditional fact-checking identified by Nyhan and Reifler (2010), and employs large language models (Argyle et al., 2023) to simulate counterfactual attributions while mitigating potential biases (Zhang et al., 2018; González-Sendino et al., 2024). Examples of yet-to-be-explored potential applications range from climate change issues to trade policy discourses to demonstrate the framework's ability to identify boundary conditions of persuasive validity across polarized groups.