Artificial Intelligence and Machine Learning at the Intersection of Privacy and Archives

Iori Khuhro; Erin Gilmore; Jim Suderman; Darra L. Hofman

Artificial Intelligence and Machine Learning at the Intersection of Privacy and Archives

Data publikacji: 03.12.2024

Archeion, 2024, 125, s. 55 - 78

https://doi.org/10.4467/26581264ARC.24.006.20201

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Tytuły

Artificial Intelligence and Machine Learning at the Intersection of Privacy and Archives

Abstrakt

As records are increasingly born digital – and thus, at least ostensibly, potentially much more accessible – archivists find themselves struggling to enable general access while providing appropriate privacy protections for the torrent of records being transferred to their care. In this article, the authors report the results of an integrative literature review study, examining the intersection of AI, archives, and privacy in terms of how archives are currently coping with these challenges and what role(s) AI might play in addressing privacy in archival records. The study revealed three major themes: 1) the challenges of – and possibilities beyond – defining “privacy” and “AI”; 2) the need for context-sensitive ways to manage privacy and access decisions; and 3) the lack of adequate “success measures” for ensuring the actual fitness for purpose of privacy AI solutions in the archival context.

W miarę tego, jak dokumentacja w coraz większym stopniu tworzona jest w formacie cyfrowym – a tym samym, przynajmniej pozornie, jest potencjalnie bardziej dostępna – archiwiści zmagają się z zapewnieniem powszechnego dostępu do niej, przy jednoczesnym zagwarantowaniu odpowiedniej ochrony prywatności w odniesieniu do ogromnej liczby dokumentów przekazywanych pod ich opiekę. W niniejszym artykule autorzy przedstawiają wyniki przeglądu literatury, badając obszar styku pomiędzy sztuczną inteligencją (AI) a archiwami i prywatnością pod kątem tego, jak placówki archiwalne radzą sobie współcześnie z tymi wyzwaniami i jaką rolę może odegrać sztuczna inteligencja w ochronie prywatności w archiwach. Badanie ujawniło trzy główne obszary tematyczne: 1) wyzwania związane z definicjami „prywatności” i „sztucznej inteligencji” oraz otwierające się nowe możliwości; 2) potrzebę powstania wrażliwych na kontekst sposobów zarządzania prywatnością i decyzjami dotyczącymi dostępu; oraz 3) brak odpowiednich „mierników sukcesu”, które gwarantowałyby faktyczną przydatność rozwiązań AI chroniących prywatność w kontekście archiwalnym.

Słowa kluczowe

archival science, privacy, artificial intelligence, machine learning, named entity recognition

archiwistyka, prywatność, sztuczna inteligencja, uczenie maszynowe, rozpoznawanie nazwanych jednostek

Podziękowania

The authors are grateful that this work was supported by International Research on Permanent Authentic Records in Electronic Systems (InterPARES) Trust AI, an international research partnership led by Drs. Luciana Duranti and Muhammad Abdul-Mageed, University of British Columbia. InterPARES Trust AI is supported in part by funding from the Social Sciences and Humanities Research Council of Canada (SSHRC). The authors would like to thank Kisun Kim (Okanagan College) and Carlos Quevedo, previous InterPARES Trust AI Graduate Research Assistants, for their contribution to this work.

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