InterPARES Trust AI (2021-2026) is a multi-national interdisciplinary project aiming to design, develop, and leverage Artificial Intelligence to support the ongoing availability and accessibility of trustworthy public records by forming a sustainable, ongoing partnership producing original research, training students and other highly qualified personnel (HQP), and generating a virtuous circle between academia, archival institutions, government records professionals, and industry, a feedback loop reinforcing the knowledge and capabilities of each party. The I Trust AI goals are to: - Identify specific AI technologies that can address critical records and archives challenges; - Determine the benefits and risks of using AI technologies on records and archives; - Ensure that archival concepts and principles inform the development of responsible AI; and - Validate outcomes from Objective 3 through case studies and demonstrations.

InterPARES Trust Artificial Intelligence

Feliciati Pierluigi;Frontoni Emanuele;Paolanti Marina
2021-01-01

Abstract

InterPARES Trust AI (2021-2026) is a multi-national interdisciplinary project aiming to design, develop, and leverage Artificial Intelligence to support the ongoing availability and accessibility of trustworthy public records by forming a sustainable, ongoing partnership producing original research, training students and other highly qualified personnel (HQP), and generating a virtuous circle between academia, archival institutions, government records professionals, and industry, a feedback loop reinforcing the knowledge and capabilities of each party. The I Trust AI goals are to: - Identify specific AI technologies that can address critical records and archives challenges; - Determine the benefits and risks of using AI technologies on records and archives; - Ensure that archival concepts and principles inform the development of responsible AI; and - Validate outcomes from Objective 3 through case studies and demonstrations.
2021
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11393/285692
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