Artificial Intelligence and Archival practice: Transforming Management, Preservation, and Access in the Digital Age

Abstract

The unprecedented growth of digital information and the increasing complexity of archival collections have compelled archival institutions worldwide to explore innovative technological solutions for records management, preservation, and access. Among these emerging technologies, artificial intelligence (AI) has attracted significant attention due to its capacity to automate processes, improve efficiency, and enhance access to archival materials. This paper critically examines the role of artificial intelligence in transforming archival management, preservation, and access within contemporary archival environments. Drawing upon current literature from archival science, information studies, digital humanities, and computer science, the study investigates the opportunities, challenges, and implications of AI adoption in archives. The paper reveals that AI technologies, including machine learning, natural language processing, computer vision, and optical character recognition, are increasingly employed to support archival appraisal, automated classification, metadata generation, digital preservation, and intelligent information retrieval. These technologies have demonstrated considerable potential in reducing processing backlogs, improving discoverability, enhancing user engagement, and facilitating large-scale digitization projects. Furthermore, AI-driven analytical tools are enabling new forms of archival research by uncovering hidden patterns and relationships within archival collections. Despite these benefits, the integration of AI into archival practice raises important ethical, legal, and professional concerns. Issues relating to algorithmic bias, transparency, accountability, privacy, authenticity, and contextual integrity challenge traditional archival principles and require careful consideration. The paper argues that while AI presents transformative opportunities for archives, its implementation must be guided by robust governance frameworks, ethical standards, and professional oversight. The study concludes that AI should be regarded as a complementary technology that augments rather than replaces the expertise of archivists. Future archival practice will depend on the successful integration of technological innovation with the enduring values of archival science, including authenticity, reliability, provenance, accountability, and equitable access.