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...
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MDPI AG
2025-07-01
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| Online Access: | https://www.mdpi.com/2673-2688/6/7/147 |
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| author | Christophe Faugere |
| author_facet | Christophe Faugere |
| author_sort | Christophe Faugere |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-1ffe53f55f374dccb840d6b43aa5a4d7 |
| institution | Kabale University |
| issn | 2673-2688 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
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| series | AI |
| spelling | doaj-art-1ffe53f55f374dccb840d6b43aa5a4d72025-08-20T03:55:48ZengMDPI AGAI2673-26882025-07-016714710.3390/ai6070147Quantifying Claim Robustness Through Adversarial Framing: A Conceptual Framework for an AI-Enabled Diagnostic ToolChristophe Faugere0Department of Finance, Economics and Accounting, Kedge Business School, 33405 Talence, FranceObjectives: 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.https://www.mdpi.com/2673-2688/6/7/147claim robustnessadversarial testingideological polarizationAI validationepistemic diagnosticsDevil’s advocate |
| spellingShingle | Christophe Faugere Quantifying Claim Robustness Through Adversarial Framing: A Conceptual Framework for an AI-Enabled Diagnostic Tool AI claim robustness adversarial testing ideological polarization AI validation epistemic diagnostics Devil’s advocate |
| title | Quantifying Claim Robustness Through Adversarial Framing: A Conceptual Framework for an AI-Enabled Diagnostic Tool |
| title_full | Quantifying Claim Robustness Through Adversarial Framing: A Conceptual Framework for an AI-Enabled Diagnostic Tool |
| title_fullStr | Quantifying Claim Robustness Through Adversarial Framing: A Conceptual Framework for an AI-Enabled Diagnostic Tool |
| title_full_unstemmed | Quantifying Claim Robustness Through Adversarial Framing: A Conceptual Framework for an AI-Enabled Diagnostic Tool |
| title_short | Quantifying Claim Robustness Through Adversarial Framing: A Conceptual Framework for an AI-Enabled Diagnostic Tool |
| title_sort | quantifying claim robustness through adversarial framing a conceptual framework for an ai enabled diagnostic tool |
| topic | claim robustness adversarial testing ideological polarization AI validation epistemic diagnostics Devil’s advocate |
| url | https://www.mdpi.com/2673-2688/6/7/147 |
| work_keys_str_mv | AT christophefaugere quantifyingclaimrobustnessthroughadversarialframingaconceptualframeworkforanaienableddiagnostictool |