Abstract:

[Purpose/Significance] As artificial intelligence (AI) systems are being widely deployed across diverse domains such as education, healthcare, and public governance, the absence of standardized metadata specifications has led to fragmented descriptions, inconsistent documentation, and difficulties in model evaluation and reuse. This study aims to address the pressing issues of opacity, lack of interpretability, and poor traceability in current AI models, which have increasingly become obstacles to the development of transparent and responsible AI. To overcome these challenges, this study proposes the establishment of a unified metadata specification for AI models to enhance their discoverability, transparency, interoperability, and reusability, thereby advancing the development of trustworthy AI and facilitating effective model governance. [Method/Process] Grounded in metadata quality assessment theory and lifecycle theory, the study adopted a combination of research methods, including literature review, comparative analysis of existing specifications, and questionnaire surveys.We first conducted a systematic examination of domestic and international practices related to AI model metadata specifications to identify representative standards, frameworks, and implementation approaches. Through comparative analysis, the study investigated the structure, element organization, and semantic relationships of different specifications, highlighting their similarities, differences, and areas for improvement. Meanwhile, a targeted questionnaire survey was administered to researchers, developers, and practitioners to explore user awareness, perceptions, practical experiences, and specific needs regarding metadata specification and interoperability. Based on these findings, the study ultimately proposed a lifecycle-oriented framework for metadata specification construction, ensuring that it aligns with the key stages of AI model development, deployment, evaluation, and governance. [Results/Conclusions] The findings reveal that, although users generally recognize the importance of metadata specifications for AI models, they are unaware of of the existing specifications. The current AI model metadata specifications have significant shortcomings in terms of element naming, structural organization, and descriptive granularity. These shortcomings hinder the effective sharing and reuse of model information. In response, the study proposed a comprehensive metadata framework encompassing key entities such as models, datasets, algorithms, technical features, performance evaluations, risks and ethics, legal information, and related resources, as well as the semantic relationships among these entities. The research concluded that establishing a unified metadata specification for AI models not only contributes to effective information management and cross-platform interoperability, but also serves as a critical infrastructure that links technology, ethics, and governance. As the metadata specification system matures and gains wider industry adoption, AI models will become increasingly controllable and trustworthy. This will promote a more regulated, collaborative, sustainable and integrated AI ecosystem.

Key words: AI models, model transparency, metadata specification, AI