The algorithmic authenticity paradox: toward a cultural trust calibration framework for AI-mediated strategic communication in high-context bilingual societies

Abstract

This rapid integration of generative AI into strategic communication has spurred a growing body of research on AI authorship disclosure, trust, and authenticity. Theoretical models, which include the Machine Heuristic, Trust Calibration, and Source Credibility frameworks, were developed and tested in Western, low-context, monolingual cultural environments. They do not adequately address the cultural and linguistic dynamics in audience responses to AI-mediated communication in high-context bilingual societies. Therefore, there is a need for a new theoretical model that can better explain how disclosure of AI authorship interacts with cultural and linguistic variables to produce differential trust outcomes. Drawing upon emerging experimental evidence from Gulf Cooperation Council (GCC) audiences, this article presents the Cultural Trust Calibration Framework (CTCF), an integrative model that explains how disclosure of AI authorship interacts with cultural and linguistic variables to produce differential trust outcomes. The proposed model is developed based on the integration of cross-disciplinary scholarship from communication, cross-cultural psychology, and human–AI interaction research. The framework posits three primary concepts: (1) the Algorithmic Authenticity Paradox, a structural contradiction between AI transparency capabilities and AI's incapacity to manifest cultural-relational authenticity; (2) Cultural Trust Calibration, the process by which high-context culture receivers dynamically adjust their trust on the basis of linguistic, modal, and cultural cues; and (3) Bilingual Trust Asymmetry, the differential effect of AI disclosure in the same bilingual audience. The CTCF develops seven testable hypotheses for empirical research, articulates operational measures for each construct, and outlines a research agenda for the emerging field of culturally calibrated AI communication scholarship. The paper also presents ethical implications for health, government, and crisis communication in multilingual high-context environments, particularly related to culturally differentiated disclosure. The framework offers the initial systematic theoretical description of how culture and language together regulate the AI disclosure penalty established through recent experiments, broadening Western AI-trust theory to a much more extensive cultural-communicative domain.