Trusted Data Forever: Is AI the Answer?

• URL: http://arxiv.org/abs/2203.03712v2
• Date: Mon, 14 Mar 2022 14:30:20 GMT
• Title: Trusted Data Forever: Is AI the Answer?
• Authors: Emanuele Frontoni, Marina Paolanti, Tracey P. Lauriault, Michael Stiber, Luciana Duranti, Abdul-Mageed Muhammad
• Abstract summary: 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.
• Score: 5.138012450471438
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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