User insights shaping machine learning applied to archives

KASTURI, Surya, SHENFIELD, Alex and ROAST, Christopher (2024). User insights shaping machine learning applied to archives. In: CONATI, Cristina, TORRE, Ilaria and VOLPE, Gualtiero, (eds.) AVI '24: Proceedings of the 2024 International Conference on Advanced Visual Interfaces. ACM.

[img] PDF
User_Insights_shaping_Machine_Learning_Applied_to_Archives_AVI_2024.pdf - Accepted Version
Restricted to Repository staff only
Creative Commons Attribution.

Download (384kB)
Official URL:
Link to published version::


Archives hold vast amounts of historical and cultural information, but navigating and extracting knowledge can be a daunting task. Machine learning (ML) offers immense potential to unlock these archives, yet its effectiveness hinges on understanding user needs. This paper explores how user insights can shape the development and application of ML in archives. Here “user” refers to editors and publishers who are crucial part of archival sorting and publication in the company. This paper emphasizes the importance of an iterative user centred design process to guide development and ensure user acceptance and empowerment. This approach reveals the distance between user expectations and functional integrity.

Item Type: Book Section
Identification Number:
SWORD Depositor: Symplectic Elements
Depositing User: Symplectic Elements
Date Deposited: 05 Jun 2024 14:54
Last Modified: 05 Jun 2024 15:51

Actions (login required)

View Item View Item


Downloads per month over past year

View more statistics