It's the end of the archival profession as we know it, and I feel fine

Theimer, Kate

doi:10.29085/9781783302192.002

Cited by 3 publications

(7 citation statements)

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“…(2018) does not specifically discuss AI, but their suggestion that archives be understood as data collections to be “mined” is a nod to the technology that allows for the mining to occur. Theimer (2018) agrees with this sentiment, suggesting that archivists need to become data scientists. CAS provides one perspective most notably that the archival science focus on the nature of the record remains central moving forward (Marciano et al.…”

Section: Literature Reviewmentioning

confidence: 75%

“…Moss et al. (2018) and Theimer (2018) specifically address the concept of archival expertise and how it may change in the context of AI, suggesting that expertise would grow to closely resemble data science.…”

Section: Discussionmentioning

confidence: 99%

“…Much of the exiting archives literature about AI technology is focused on how AI may impact the archives field: this includes how AI may change traditional archival work such as arrangement, description and appraisal via automation (Lee, 2018); how traditional archival theory such as original order and provenance may change (Colavizza et al, 2022); and how AI technology will require archivists to learn new computational skills (Marciano et al, 2018;Payne, 2018). Moss et al (2018) and Theimer (2018) specifically address the concept of archival expertise and how it may change in the context of AI, suggesting that expertise would grow to closely resemble data science. Susskind and Susskind (2015) in The future of the professions: How technology will transform the work of human experts predict the decline of professions that require expertise like law and medicine because of the rise of advanced technology, including AI.…”

Section: Biasmentioning

confidence: 99%

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“So how do we balance all of these needs?”: how the concept of AI technology impacts digital archival expertise

Cushing

Osti²

2022

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PurposeThis study aims to explore the implementation of artificial intelligence (AI) in archival practice by presenting the thoughts and opinions of working archival practitioners. It contributes to the extant literature with a fresh perspective, expanding the discussion on AI adoption by investigating how it influences the perceptions of digital archival expertise.Design/methodology/approachIn this study a two-phase data collection consisting of four online focus groups was held to gather the opinions of international archives and digital preservation professionals (n = 16), that participated on a volunteer basis. The qualitative analysis of the transcripts was performed using template analysis, a style of thematic analysis.FindingsFour main themes were identified: fitting AI into day to day practice; the responsible use of (AI) technology; managing expectations (about AI adoption) and bias associated with the use of AI. The analysis suggests that AI adoption combined with hindsight about digitisation as a disruptive technology might provide archival practitioners with a framework for re-defining, advocating and outlining digital archival expertise.Research limitations/implicationsThe volunteer basis of this study meant that the sample was not representative or generalisable.Originality/valueAlthough the results of this research are not generalisable, they shed light on the challenges prospected by the implementation of AI in the archives and for the digital curation professionals dealing with this change. The evolution of the characterisation of digital archival expertise is a topic reserved for future research.

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Section: Literature Reviewmentioning

confidence: 75%

Section: Discussionmentioning

confidence: 99%

Section: Biasmentioning

confidence: 99%

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“So how do we balance all of these needs?”: how the concept of AI technology impacts digital archival expertise

Cushing

Osti²

2022

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“…Looking at implications of AI for the profession, [Theimer 2018] sketches a picture of the future of the profession in which numerous archive tasks can and will be taken over by machines and algorithms. This implies that archivists need to become "masters of data" but at the same time the profession has to focus more on "narrative, storytelling, meaning-making, context providing (...) outreach and education [which] will be more important than ever" [Theimer 2018]. [Rolan 2019] observe an emerging gap between archivists who are familiar with AI concepts and those who are not and stress the necessity of "retraining" to mitigate the knowledge and skills gap.…”

Section: Theoretical and Professional Considerationsmentioning

confidence: 99%

Archives and AI: An Overview of Current Debates and Future Perspectives

Colavizza

Blanke

Jeurgens

et al. 2021

J. Comput. Cult. Herit.

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The digital transformation is turning archives, both old and new, into data. As a consequence, automation in the form of artificial intelligence techniques is increasingly applied both to scale traditional recordkeeping activities, and to experiment with novel ways to capture, organise, and access records. We survey recent developments at the intersection of Artificial Intelligence and archival thinking and practice. Our overview of this growing body of literature is organised through the lenses of the Records Continuum model. We find four broad themes in the literature on archives and artificial intelligence: theoretical and professional considerations, the automation of recordkeeping processes, organising and accessing archives, and novel forms of digital archives. We conclude by underlining emerging trends and directions for future work, which include the application of recordkeeping principles to the very data and processes that power modern artificial intelligence and a more structural—yet critically aware—integration of artificial intelligence into archival systems and practice.

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“…Data science professionals are often transdisciplinary and able to bring information management and technical skills to their professions. Theimer (2018) argues that archivists are hindered without similar training. "We need to become, if we are not already, masters of data, or to use a less romantic title, data scientists.…”

Section: Transdisciplinary Appraisers Microcosms and Macrocosmsmentioning

confidence: 99%

WAKING TO NORMAL: Examining Archival Appraisal in Data-driven Society

Fitzgerald¹

2020

Preprint

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The COVID-19 environment, where the internet is the literal lifeline and livelihood of humanity, has exposed the chasm between those equipped for technological existence and those shocked by abrupt isolation. For archives, many institutions are on an unforgiving precipice of irrelevancy. The focus of this paper is not to iterate the necessity of archival theory but to examine the position of appraisal within a technologically-driven, internet society. Of significance to this evaluation is that organization, retrieval, and use of information have evolved, and users are central players in curation cycles. Also, of importance are those archives shifting to, and innovating, decentralized digital models - and thriving. A historical overview of both fields shows that appraisal falters in technical maturation and in response to changes in how society generates, captures, and retrieves information. There exist alternate paradigms for archival roles and appraisal, however, including recognizing that users derive the interpretation of information and that transdisciplinary archivists are vital. There are also developments in digital archives where access is the bedrock of the arrangement and description and the entire appraisal process.

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It's the end of the archival profession as we know it, and I feel fine

Cited by 3 publications

References 0 publications

“So how do we balance all of these needs?”: how the concept of AI technology impacts digital archival expertise

“So how do we balance all of these needs?”: how the concept of AI technology impacts digital archival expertise

Archives and AI: An Overview of Current Debates and Future Perspectives

WAKING TO NORMAL: Examining Archival Appraisal in Data-driven Society

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