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

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...

Full description

Saved in:
Bibliographic Details
Main Authors: Iori Khuhro, Erin Gilmore, Jim Suderman, Darra L. Hofman
Format: Article
Language:deu
Published: The State Archives Head Office, Poland 2024-12-01
Series:Archeion
Online Access: https://ejournals.eu/czasopismo/archeion/artykul/artificial-intelligence-and-machine-learning-at-the-intersection-of-privacy-and-archives
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0066-6041
2658-1264