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Representation and Authenticity in AI Generated, Curated, and Mediated Archives
Journal article   Peer reviewed

Representation and Authenticity in AI Generated, Curated, and Mediated Archives

Robert B. Riter, Bharat Mehra, Alex H. Poole, Zack Lischer-Katz, Sarah Tribelhorn and Travis Wagner
Proceedings of the Association for Information Science and Technology, v 62(1), pp 1655-1657
Oct 2025
DOI: https://doi.org/10.1002/pra2.1498
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https://doi.org/10.1002/pra2.1498View
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Abstract

assessment & use authenticity media archives Representation visual information
ABSTRACT Archival and information science scholars have begun to evaluate the impact of AI tools on how archival objects are created, used, and evaluated (Shinde, Kirstein, Ghosh, and Franks, 2024). Researchers have addressed a broad range of questions, including the evidential properties of AI generated archives, the use of large language models in supporting archival processing, and strategies for evaluating the authenticity and reliability of AI generated archives (Jaillant and Caputo, 2022; Reducindo and Olague, 2024; Wagner and Blewer, 2019). This initial work represented in the poster indicates a need to investigate questions pertaining to the evidential foundations and societal impacts of AI generated and mediated archives, and the potential and existing requirements for AI tools to enhance use of legacy and born‐digital archival sources. This is significant for supporting traditional archival objectives in providing for and assessing reliability and authenticity (Arias Hernández and Rockembach, 2025). These issues are present at multiple stages of the archival life cycle – records creation, access, and evaluation and use – and are introduced and discussed broadly in this poster.

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