Automatic Classification of Records and Archives as Data: A Survey of Experiments Using Machine Learning.
Saved in:
| Title: | Automatic Classification of Records and Archives as Data: A Survey of Experiments Using Machine Learning. |
|---|---|
| Authors: | Watanabe, Eduardo1 edwatanabe@protonmail.com, Sousa, Renato Tarciso Barbosa de1 |
| Source: | Knowledge Organization. Apr2026, Vol. 53 Issue 2, p1-29. 29p. |
| Subjects: | Automatic classification, Archives, Knowledge management, Archival research, Machine learning, Artificial intelligence & ethics, Artificial intelligence |
| Abstract: | This paper investigates the application of machine learning (ML) to the automatic classification of records and archives, framing it as a critical challenge in Knowledge Organization (KO). As digitization creates massive volumes of uncategorized data, the following research question arises: how can fundamental archival principles-such as provenance, original order, and hierarchical description-be translated into this new computational paradigm? This study first synthesizes, based on a multidisciplinary review of archival science, classification theory, KO, computer science, and information science, a proposal of six fundamental guidelines for the responsible application of artificial intelligence (AI) in records and archives. These guidelines connect traditional archival theory with the modern imperatives of trustworthy and explainable AI. Second, we conduct a comparative analysis of 24 published ML experiments, assessing their adherence to these guidelines. Our analysis reveals a significant and troubling disconnect. While most experiments acknowledge the principle of provenance (75.0%), they demonstrate profound neglect of guidelines related to diverse perspectives (25.0%), explainability (16.7%), and, most critically, algorithmic accountability (0.0%). The results indicate that current practices often succeed in basic content categorization but fail in the more sophisticated archival task of preserving archives' evidentiary and relational integrity by treating records as decontextualized data. The study calls for urgently developing an Archival AI Lifecycle-a framework that weaves archival principles, classification theory, and knowledge organization into AI development, safeguarding archival practice's intellectual and ethical integrity in the digital age. [ABSTRACT FROM AUTHOR] |
| Copyright of Knowledge Organization is the property of IMR Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 193667669 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Automatic Classification of Records and Archives as Data: A Survey of Experiments Using Machine Learning. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Watanabe%2C+Eduardo%22">Watanabe, Eduardo</searchLink><relatesTo>1</relatesTo><i> edwatanabe@protonmail.com</i><br /><searchLink fieldCode="AR" term="%22Sousa%2C+Renato+Tarciso+Barbosa+de%22">Sousa, Renato Tarciso Barbosa de</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Knowledge+Organization%22">Knowledge Organization</searchLink>. Apr2026, Vol. 53 Issue 2, p1-29. 29p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Automatic+classification%22">Automatic classification</searchLink><br /><searchLink fieldCode="DE" term="%22Archives%22">Archives</searchLink><br /><searchLink fieldCode="DE" term="%22Knowledge+management%22">Knowledge management</searchLink><br /><searchLink fieldCode="DE" term="%22Archival+research%22">Archival research</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence+%26+ethics%22">Artificial intelligence & ethics</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This paper investigates the application of machine learning (ML) to the automatic classification of records and archives, framing it as a critical challenge in Knowledge Organization (KO). As digitization creates massive volumes of uncategorized data, the following research question arises: how can fundamental archival principles-such as provenance, original order, and hierarchical description-be translated into this new computational paradigm? This study first synthesizes, based on a multidisciplinary review of archival science, classification theory, KO, computer science, and information science, a proposal of six fundamental guidelines for the responsible application of artificial intelligence (AI) in records and archives. These guidelines connect traditional archival theory with the modern imperatives of trustworthy and explainable AI. Second, we conduct a comparative analysis of 24 published ML experiments, assessing their adherence to these guidelines. Our analysis reveals a significant and troubling disconnect. While most experiments acknowledge the principle of provenance (75.0%), they demonstrate profound neglect of guidelines related to diverse perspectives (25.0%), explainability (16.7%), and, most critically, algorithmic accountability (0.0%). The results indicate that current practices often succeed in basic content categorization but fail in the more sophisticated archival task of preserving archives' evidentiary and relational integrity by treating records as decontextualized data. The study calls for urgently developing an Archival AI Lifecycle-a framework that weaves archival principles, classification theory, and knowledge organization into AI development, safeguarding archival practice's intellectual and ethical integrity in the digital age. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Knowledge Organization is the property of IMR Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=193667669 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.31083/KO53184 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 29 StartPage: 1 Subjects: – SubjectFull: Automatic classification Type: general – SubjectFull: Archives Type: general – SubjectFull: Knowledge management Type: general – SubjectFull: Archival research Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Artificial intelligence & ethics Type: general – SubjectFull: Artificial intelligence Type: general Titles: – TitleFull: Automatic Classification of Records and Archives as Data: A Survey of Experiments Using Machine Learning. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Watanabe, Eduardo – PersonEntity: Name: NameFull: Sousa, Renato Tarciso Barbosa de IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: Apr2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 09437444 Numbering: – Type: volume Value: 53 – Type: issue Value: 2 Titles: – TitleFull: Knowledge Organization Type: main |
| ResultId | 1 |
