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<p>The benefits of AI technologies in archival preservation are well recognised, though questions remain about their integration into existing processes. AI also shows promise for enhancing user experience and discovery in accessing born-digital materials. However, a limited understanding of the diverse methodological needs surrounding born-digital access risks the creation of one-size-fits-all solutions that suit certain approaches and research questions better than others. This article reviews current efforts in born-digital access and applies the Garbage Can Model from organisation theory to conceptualise the challenge of developing AI-based tools for multiple user types, highlighting the iterative and often decentralised nature of multi-stakeholder decision-making. We address this challenge by creating four born-digital archival user types—the aggregator, the synthesiser, the fact finder, and the narrator—each with distinct motivations and research approaches. Finally, we identify some new opportunities for stakeholders to inform how AI-based tools can be developed to better meet the variety of methodological needs that exist in relation to born-digital archives.</p>

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Conceptualising methodological diversity among born-digital users: insights from the garbage can model

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月6日

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  • Adam Nix,
  • Stephanie Decker,
  • David A. Kirsch

摘要

The benefits of AI technologies in archival preservation are well recognised, though questions remain about their integration into existing processes. AI also shows promise for enhancing user experience and discovery in accessing born-digital materials. However, a limited understanding of the diverse methodological needs surrounding born-digital access risks the creation of one-size-fits-all solutions that suit certain approaches and research questions better than others. This article reviews current efforts in born-digital access and applies the Garbage Can Model from organisation theory to conceptualise the challenge of developing AI-based tools for multiple user types, highlighting the iterative and often decentralised nature of multi-stakeholder decision-making. We address this challenge by creating four born-digital archival user types—the aggregator, the synthesiser, the fact finder, and the narrator—each with distinct motivations and research approaches. Finally, we identify some new opportunities for stakeholders to inform how AI-based tools can be developed to better meet the variety of methodological needs that exist in relation to born-digital archives.