Gazi Journal of Economics and Business 2548-0162 Aydın KARAPINAR 10.30855/gjeb.2025.11.3.009 Digital Marketing Dijital Pazarlama A multi-agent framework for verifiable AIGC licensing in digital ecosystems Dijital ekosistemlerde doğrulanabilir AIGC lisanslaması için çoklu ajan çerçevesi https://orcid.org/0000-0002-6934-4421 Uysal Mevlüt GAZİ ÜNİVERSİTESİ 10 27 2025 11 3 327 346 08 02 2025 10 09 2025 Copyright © 2015, Gazi Journal of Economics and Business 2015 Gazi Journal of Economics and Business

The emergence of AI-generated content (AIGC) presents intricate difficulties concerning authorship, ownership, and validation of digital assets. These difficulties reveal a significant deficiency in existing governance frameworks, especially regarding the licensing and traceability of synthetic media. This study introduces a modular, multi-agent framework (MAG-AIGC) that utilizes Retrieval-Augmented Generation (RAG) and blockchain technologies to automate content registration, license interpretation, and provenance verification. The suggested architecture was formulated using the Design Science Research (DSR) approach and assessed over 30 relevant AIGC-licensing scenarios. Experimental findings indicate that the system attains up to 90% accuracy in licensing compliance detection, concurrently ensuring minimal latency and transaction costs. In addition to its technological contributions, the framework enhances trust, transparency, and accountability within the emerging AIGC ecosystem, providing actionable insights for regulators, creators, and platform developers to implement verifiable digital governance mechanisms and ensure reliability in AIGC-based commerce.

Yapay zekâ tarafından üretilen içeriklerin (AIGC) ortaya çıkışı, dijital varlıkların yazarlığı, mülkiyeti ve doğrulanması açısından karmaşık sorunlar doğurmuştur. Bu sorunlar, özellikle sentetik medyanın lisanslanması ve izlenebilirliği konusunda mevcut yönetişim çerçevelerinde önemli bir yetersizliği açığa çıkarmaktadır. Bu çalışma, içerik kaydını, lisans yorumlamasını ve köken doğrulamasını otomatikleştirmek için bilgiye dayalı üretim (Retrieval-Augmented Generation, RAG) ve blok zinciri teknolojilerini kullanan modüler, çoklu aracılı bir çerçeve (MAG-AIGC) önermektedir. Önerilen mimari, Tasarım Bilimi Araştırması (Design Science Research, DSR) yaklaşımıyla geliştirilmiş ve 30’dan fazla AIGC lisanslama senaryosu üzerinde değerlendirilmiştir. Deneysel bulgular, sistemin lisans uyumluluğu tespitinde %90’a varan doğruluk oranına ulaştığını ve aynı anda düşük gecikme süresi ile işlem maliyeti sağladığını göstermektedir. Teknolojik katkılarının ötesinde, bu çerçeve gelişmekte olan AIGC ekosisteminde güveni, şeffaflığı ve hesap verebilirliği artırmakta, düzenleyiciler, içerik üreticileri ve platform geliştiricileri için doğrulanabilir dijital yönetişim mekanizmalarının uygulanması ve AIGC tabanlı ticarette güvenilirliğin sağlanması adına uygulanabilir içgörüler sunmaktadır.

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