Bartliff, Z. , Kim, Y. , Naja, I. and Ruthven, I. (2026) “Participatory harm auditing of generative AI in cataloguing and description” workshop Aug 2025. [Data Collection]
Collection description
The great variety and volume of data as well as the various methods for analysing data are an increasingly prominent and resource intensive element to cultural heritage collections management. Consequently, there is an understandable drive to explore the numerous ways in which generative AI might streamline these activities, allowing professionals the time they need to engage with activities that require a human hand. In fact, there have been a wide variety of examples where institutions have employed generative AI within limited contexts, but to great effect. Widespread application, however, is hindered through ongoing debates about the ethics and practicalities of implementing generative AI models. Particularly, the opaqueness of how AI models are created, trained and produce content makes it challenging for information professionals to trust the integrity of any arising outputs. In the proposed workshop participants will have the opportunity to understand and apply audit methodologies developed within our research project and provide views towards further co-design. The methodology has been created to support the archives and records community to critically and consistently assess the quality of generative AI outputs for their viability of use in diverse contexts, such as cataloguing and description. The provision of such a methodology is a significant step towards where things could be in relation to responsible technological integrations and innovations with cultural heritage data.
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| Keywords: | Cultural Heritage; AI; Participatory Auditing |
| College / School: | College of Arts & Humanities > School of Humanities |
| Date Deposited: | 30 Jun 2026 08:23 |
| URI: | https://researchdata.gla.ac.uk/id/eprint/2315 |
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