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RIS BIB ENDNOTEData publikacji: 25.11.2024
Archeion, 2024, 125, s. 33 - 54
https://doi.org/10.4467/26581264ARC.24.007.20202Autorzy
Envisioning Archival Images with Artificial Intelligence
The literature review explores the role of Artificial Intelligence (AI) in enhancing access to and management of photographic archives. As digital and analog photographs proliferate in archival institutions, traditional approaches to organizing and describing these materials are increasingly inadequate. The review highlights the potential of AI, particularly computer vision (CV), to address the challenges associated with processing large volumes of digital images. CV algorithms, such as object detection and image classification, can automate tasks like image metadata generation, offering archivists new tools for organizing collections more efficiently. However, the adoption of AI in archival practice raises important ethical concerns, particularly regarding biases inherent in AI training datasets and technologies like facial recognition. Through various case studies, the review demonstrates that interdisciplinary collaboration between archivists, AI specialists, and scholars is crucial to developing effective AIdriven solutions. Projects like CAMPI and the Finnish Wartime Photograph Archive illustrate the practical benefits of AI, while emphasizing the need for archivists to develop AI and visual literacy. This review serves as a foundational resource for archival scholars and practitioners interested in utilizing AI to improve access to photographic archives.
Angelova L., Ogden B., Craig J., Chandrapal H., Manandhar D., Deep discoveries: A towards a national collection foundation project final report, 2021, https://doi.org/10.5281/zenodo.5710412 [access: 7.11.2024].
Archives, Access and Artificial Intelligence: Working with Born-Digital and Digitized Archival Collections, ed. L. Jaillant, Bielefeld 2022.
Arnold T., Ayers N., Madron J., Nelson R, Tilton L., Visualizing a Large Spatiotemporal Collection of Historic Photography with a Generous Interface. Presented at the 5th Workshop on Visualization for the Digital Humanities, 2020, https://doi.org/10.48550/arXiv.2009.02242 [access: 7.11.2024].
Arnold T., Leonard P., Tilton L., Knowledge Creation Through Recommender Systems, “Digital Scholarship in the Humanities” 2017, vol. 32, pp. 151–157, https://doi.org/10.1093/llc/fqx035 [access: 7.11.2024].
Arnold T., Tilton L., Distant Viewing: Computational Exploration of Digital Images, Cambridge 2023. Aske K., Giardinetti M., (Mis)matching Metadata: Improving Accessibility in Digital Visual Archives through the EyCon Project, “Journal on Computing and Cultural Heritage” 2023, vol. 16, issue 4, article 76, pp. 1–20, https://doi.org/10.1145/3594726 [access: 7.11.2024].
Bakker R., Rowan K., Hu L., Guan B., Liu P., Li Z., He R., Monge C., AI for archives: Using facial recognition to enhance metadata, “Works of the FIU Libraries” 2020, vol. 93, pp. 1–15, https://digitalcommons.fiu.edu/glworks/93 [access: 7.11.2024].
Bushey J., Born digital images as reliable and authentic records, master’s thesis: University of British Columbia, 2005, https://doi.org/10.14288/1.0092057 [access: 7.11.2024].
Bushey J., He Shoots, He Stores: New Photographic Practice in the Digital Age, “Archivaria” 2008, vol. 65, issue 1, pp. 125–149, https://archivaria.ca/index.php/archivaria/article/view/13172 [access: 7.11.2024].
Bushey J., The archival trustworthiness of digital photographs in social media platforms, doctoral thesis: University of British Columbia, 2016, https://doi.org/10.14288/1.0300440 [access: 7.11.2024].
Chumachenko K., Mannisto A., Iosifidis A., Raitoharju J., Machine learning based analysis of Finnish world war II photographers, “IEEE Access” 2020, vol. 8, pp. 144184-144196, https://doi.org/10.1109/ACCESS.2020.3014458 [access: 7.11.2024].
Colavizza G., Blanke T., Jeurgens C., Noordegraaf J., Archives and AI: An overview of current debates and future perspectives, “Journal on Computing and Cultural Heritage” 2021, vol. 15, no. 1, article 4, pp. 1–15, https://doi.org/10.1145/3479010 [access: 7.11.2024].
Conway P., Punzalan R.L., Fields of Vision: Toward a New Theory of Visual Literacy for Digitized Archival Photographs, “Archivaria” 2011, vol. 71, pp. 63–97, https://archivaria.ca/index.php/archivaria/article/view/13331 [access: 7.11.2024].
Cox J., Tilton L., The digital Public Humanities: Giving New Arguments and New Ways to Argue, „Review of Communication” 2019, vol. 19, issue 2, pp. 127–146, https://doi.org/10.1080/15358593.2019.1598569 [access: 7.11.2024].
Delaney J., An Inconvenient Truth? Scientific Photography and Archival Ambivalence, “Archivaria” 2008, vol. 65, pp. 75–95, https://archivaria.ca/index.php/archivaria/article/view/13169 [access: 7.11.2024].
Dentler J., Workshop Report: Multimodal Visual Similarity Algorithms and Digitized Photo Archives. EyCon Visual AI and Early Conflict Photography, Blog Post, 2023, https://eycon.hypotheses.org/1539 [access: 7.11.2024].
Eiler F., Graf S., Dorner W., Artificial intelligence and the automatic classification of historical photographs [in:] Proceedings of the Sixth International Conference on Technological Ecosystems for Enhancing Multiculturality, ed. F.J. García-Peñalvo, New York 2018, pp. 852–856, https://doi.org/10.1145/3284179.3284324 [access: 7.11.2024].
Fewster K., Case Study: Testing computational Archival Science frameworks using AI tools in analyzing the Spelman College Archives photograph collection, InterPARES Trust AI, 2024, https://interparestrustai.org/assets/public/dissemination/ProctorCaseStudy.pdf [access: 7.11.2024].
Fewster K., Case Study: The Endangered Archives Programme’s use of AI tools in evaluating Jacques Toussele’s Cameroonian photography archives, InterPARES Trust AI, 2024, https://interparestrustai.org/assets/public/dissemination/ZeitlynInterviewCaseStudy.pdf [access: 7.11.2024].
Han X.Y., Papyan V., Prokop E., Donoho D.L., Johnson C.R., Chapter 1: Artificial intelligence and discovering the digitized photoarchive [in:] Digital Humanities Research, ed. L. Jailant, Bielefeld 2022, pp. 29–60, https://doi.org/10.14361/9783839455845-002 [access: 7.11.2024].
Leary W.H., The archival appraisal of photographs: a RAMP study with guidelines, Paris 1985, https://unesdoc.unesco.org/ark:/48223/pf0000063749 [access: 7.11.2024].
Lincoln M., Corrin J., Davis E., Weingart S.B., CAMPI: computer-aided metadata generation for photo archives initiative, Pittsburgh 2020, https://doi.org/10.1184/R1/12791807.v2 [access: 7.11.2024].
Long D., Magerko B., What is AI literacy? Competencies and design considerations [in:] CHI’20 : proceedings of the 2020 CHI Conference on Human Factors in Computing Systems: April 25–30, 2020, Honolulu, HI, USA, New York 2020, pp. 1–16.
Mallick S., EyCon: What are we doing with Machine Learning and Computer Vision, EYCON Blog Post, 2022, https://eycon.hypotheses.org/1020 [access: 7.11.2024].
Mannheimer S., Rossmann D., Clark J., Shorish Y., Bond N., Scates Kettler H., Sheehey B., Young S.W.H., Introduction to the Special Issue: Responsible AI in Libraries and Archives, “Journal of eScience Librarianship” 2024, vol. 13, no. 1, https://publishing.escholarship.umassmed.edu/jeslib/article/id/860/ [access: 7.11.2024].
Milleville K., Broeck A.V.D., Vanderperren N., Vissers,R., Priem M., Van De Weghe N., Verstockt S., Enriching image archives via facial recognition, “Journal on Computing and Cultural Heritage” 2023, vol. 16, no. 4, pp. 1–18, https://doi.org/10.1145/3606704 [access: 7.11.2024].
O’Donnell L., Towards Total Archives: The Form and Meaning of Photographic Records, “Archivaria” 1994, vol. 38, pp. 105–118, https://archivaria.ca/index.php/archivaria/article/view/12028 [access: 7.11.2024].
Oestreicher C., Reference and Access for Archives and Manuscripts, Chicago 2020.
Proctor J., Marciano R., An AI-assisted framework for rapid conversion of descriptive photo metadata into linked data [in:] IEEE International Conference on Big Data (Big Data), 2021, pp. 2255–2261, https://doi.org/10.1109/BigData52589.2021.9671715 [access: 7.11.2024].
Ritzenthaler M.L., Vogt-O’Connor D., Photographs: archival care and management, Chicago 2006, https://search.worldcat.org/title/Photographs-:-archival-care-and-management/oclc/70175019 [access: 7.11.2024].
Rockembach M., AI Literacy: A Muse for Records Management and Archival Professionals [in:] Artificial Intelligence and Documentary Heritage. SCEaR Newsletter 2024, Special Issue 2024, eds. L. Duranti, C. Rogers, 2024, pp. 90–95, https://interparestrustai.org/assets/public/dissemination/SCEaRNewsletter SpecialIssue2024ArtificialIntelligence.pdf [access: 7.11.2024].
Schwartz J.M., Coming to Terms with Photographs: Descriptive Standards, Linguistic “Othering” and the Margins of Archivy, “Archivaria” 2002, vol. 54, pp. 142–171, https://archivaria.ca/index.php/archivaria/article/view/12861 [access: 7.11.2024].
Schwartz J.M., Records of Simple Truth and Precision: Photography, Archives, and the Illusion of Control, “Archivaria” 2000, vol. 50, pp. 1–40, https://archivaria.ca/index.php/archivaria/article/view/12763 [access: 7.11.2024].
Schwartz J.M., “We Make Our Tools and Our Tools Make Us”: Lessons from Photographs for the Practice, Politics, and Poetics of Diplomatics, “Archivaria” 1995, vol. 40, pp. 40–74, https://archivaria.ca/index.php/archivaria/article/view/12096 [access: 7.11.2024].
Wevers M., Smits T., The visual digital turn: Using neural networks to study historical images, “Digital Scholarship in the Humanities” 2020, vol. 35, issue 1, pp. 194–207, https://doi.org/10.1093/llc/fqy085 [access: 7.11.2024].
AEOLIAN Network. Homepage, https://www.aeolian-network.net/ [access: 7.11.2024].
AURA Network. Homepage, https://www.aura-network.net/ [access: 7.11.2024].
Github. katerynaCh / Finnish-WW2-photographers-analysis, https://github.com/katerynaCh/Finnish-WW2-photographers-analysis [access: 7.11.2024].
InterPARES Trust AI. Home Page, https://interparestrustai.org/ [access: 7.11.2024].
LUSTRE. Home Page, https://lustre-network.net/team/ [access: 7.11.2024].
The EyCon project (eng.). (n.d.), https://eycon.hypotheses.org/ [access: 7.11.2024].
Informacje: Archeion, 2024, 125, s. 33 - 54
Typ artykułu: Oryginalny artykuł naukowy
Tytuły:
San José State University
Stany Zjednoczone Ameryki
Publikacja: 25.11.2024
Finansowanie artykułu:
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AngielskiLiczba wyświetleń: 166
Liczba pobrań: 40