Privacy Preservation in AI-Enhanced Journalism: A Lifecycle-Aware Framework for Technological Innovation and Trustworthy Global Media Communication

Abstract

Purpose: This manuscript develops and tests a lifecycle-aware framework for privacy preservation in AI-enhanced journalism. It examines how privacy-preserving AI governance, lifecycle privacy risk awareness, and technological innovation capability are associated with trustworthy global media communication in news organizations using generative AI, automated analytics, personalization, translation, verification, and newsroom decision-support tools. The study responds to recent evidence that AI is becoming embedded in news production and distribution while public trust in AI-generated or AI-assisted news remains fragile.

Method: A cross-sectional, survey-based design was used with 384 valid responses from AI-engaged news professionals across six world regions. The questionnaire was built from recent AI governance, journalism ethics, privacy, and trustworthy AI frameworks, and the model was analyzed using partial least squares structural equation modelling. Because the design is cross-sectional, the structural paths are interpreted as theory-guided associations rather than definitive causal effects.

Findings: The analysis indicates that privacy-preserving AI governance is positively associated with trustworthy global media communication and lifecycle privacy risk awareness. Lifecycle privacy risk awareness is also positively associated with trustworthy global media communication and mediates the governance-trust pathway. Technological innovation capability strengthens the relationship between privacy-preserving AI governance and trustworthy global media communication, suggesting that privacy protection becomes more credible when newsrooms possess practical capabilities for secure data pipelines, model documentation, consent-aware workflow design, privacy-enhancing technologies, and human editorial oversight.

Implications: The manuscript contributes a lifecycle-aware model that links newsroom innovation with privacy-by-design, accountable AI, and global communication trust. It provides measurement items, hypotheses, reliability and validity tests, discriminant validity results, model fit statistics, and path analysis. Practically, the framework helps editors, product teams, data journalists, legal teams, and media regulators translate broad AI ethics principles into operational privacy safeguards across data collection, sourcing, model development, publication, audience analytics, archiving, and cross-border communication.