Transparent but Trustworthy: Reconciling the AI Disclosure Paradox in Corporate and Customer Communication

Abstract

Regulators and public interest activists increasingly advocate for full transparency in customer-facing AI. However, an empirical meta-synthesis of 17 studies (N=14,820) reveals a critical transparency dilemma: uncontextualized raw AI disclosures trigger negative persuasion knowledge, reducing consumer trust by 1.12 to 1.48 points on a 7-point scale. This trust penalty erodes brand trust, perceived competence, and institutional legitimacy. Critically, this decline stems from unframed transparency rather than algorithm aversion itself. Combining AI disclosures with explicit Human-in-the-Loop (HITL) cues protects consumer trust and restores brand authenticity (M=5.56 and M=5.68). A cross-sectoral analysis across banking, healthcare, retail, aviation, and technology highlights widespread governance gaps, with 82% of top-performing firms asserting AI use without addressing AI governance. Based on a structural equation modeling path model (R2=0.58), the study provides a business communication framework with actionable guidance for practitioners and regulators.