Santa Barbara Statement on Collections as Data --- Always Already Computational: Collections as Data

Padilla, Thomas; Allen, Laurie; Frost, Hannah; Potvin, Sarah; Roke, Elizabeth Russey; Varner, Stewart

doi:10.5281/zenodo.3066209

Cited by 1 publication

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Section: Literature Reviewmentioning

confidence: 99%

“…Computational skills may aid in this endeavour. At the least, CAS skills and knowledge can help archivists understand how their users wish to access data: as large datasets, a perspective shared by the project Always Already Computational, resulting in the redaction of the Santa Barbara Statement on collections as data (Padilla et al, 2019b). Running between 2016 and 2018, the initiative was centred on understanding and mapping "the current and potential approaches to developing cultural heritage collections that support computationally-driven research and teaching" (Padilla et al, 2019a).…”

Section: Ai Technology and Digital Archival Expertisementioning

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: 99%

Section: Ai Technology and Digital Archival Expertisementioning

confidence: 99%