CYAIFeb 16, 2022

Trusted Data Forever: Is AI the Answer?

arXiv:2203.03712v24 citations
Originality Synthesis-oriented
AI Analysis

This addresses the problem of long-term digital preservation for archival institutions and society, but it is incremental as it builds on existing archival practices with AI integration.

The paper tackles the challenge of preserving and accessing vast amounts of trusted archival data forever by exploring whether AI can support this goal, presenting preliminary results from an international research partnership that identifies AI technologies, assesses benefits and risks, integrates archival principles, and validates outcomes through case studies.

Archival institutions and programs worldwide work to ensure that the records of governments, organizations, communities, and individuals are preserved for future generations as cultural heritage, as sources of rights, and as vehicles for holding the past accountable and to inform the future. This commitment is guaranteed through the adoption of strategic and technical measures for the long-term preservation of digital assets in any medium and form - textual, visual, or aural. Public and private archives are the largest providers of data big and small in the world and collectively host yottabytes of trusted data, to be preserved forever. Several aspects of retention and preservation, arrangement and description, management and administrations, and access and use are still open to improvement. In particular, recent advances in Artificial Intelligence (AI) open the discussion as to whether AI can support the ongoing availability and accessibility of trustworthy public records. This paper presents preliminary results of the InterPARES Trust AI (I Trust AI) international research partnership, which aims to (1) identify and develop specific AI technologies to address critical records and archives challenges; (2) determine the benefits and risks of employing AI technologies on records and archives; (3) ensure that archival concepts and principles inform the development of responsible AI; and (4) validate outcomes through a conglomerate of case studies and demonstrations.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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