The rapid integration of artificial intelligence (AI) into journalistic workflows has intensified the need for structured institutional governance frameworks capable of moving beyond principle-based declarations toward measurable accountability architectures. This study develops and operationalizes the Artificial Intelligence Governance Maturity Index (AGMI), a weighted, three-dimensional assessment model designed to evaluate governance capacity within journalism institutions. The framework comprises fifteen indicators distributed across Awareness, Auditing, and Accountability dimensions, aggregated through a theoretically justified weighting structure. To enhance methodological rigor, the indicator architecture was subjected to structured expert validation to assess conceptual relevance and dimensional alignment. In addition, an analytical statistical reliability demonstration using simulated institutional profiles was conducted to evaluate internal consistency and structural coherence. Results indicate satisfactory dimensional reliability and composite stability under varied institutional conditions. By translating normative AI governance principles into a structured maturity measurement instrument, the AGMI contributes a replicable benchmarking tool for institutional self-assessment and cross-organizational comparison. While large-scale empirical deployment remains a direction for future research, the study establishes a robust conceptual and methodological foundation for AI governance maturity evaluation in journalism ecosystems.
Artificial Intelligence Governance; Algorithmic Accountability; Journalism and AI; Institutional Maturity Models; Algorithmic Auditing; Media Ethics; AI Regulation; Governance Measurement.
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