Archives and records management in machine learning technologies context: a research hypothesis on university records

Résumé 0

Purpose - This paper explores the goal and potential of integrating machine learning technologies into archives and records management practices. As the volume and complexity of digital records continue to grow, traditional methods of organising, classifying, and managing records face new challenges. Machine learning technologies offer opportunities to revolutionise how records are maintained, accessed, and used. Design/methodology/approach - The relationship between records and archive management and machine learning practices is presented through the literature. This paper proposes a case study implementation of machine learning practices for the subject classification of records at the University of West Attica. Findings - This paper presents a research hypothesis placing the subject classification of records at the center of the discussion. It highlights the necessity of deepening the standardisation of government actions record management processes. Originality/value - By exploring this topic, the paper seeks to contribute to a deeper understanding of the transformative role that machine learning technologies can play in archives and records management and to inform future practices and decision-making in the field. It is also the first theoretical part of an ongoing research project on the subject classification of the University of West Attica records.