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.

PDF User_Insights_shaping_Machine_Learning_Applied_to_Archives_AVI_2024.pdf - Accepted Version Restricted to Repository staff only Creative Commons Attribution. Download (384kB)

Abstract

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:	https://doi.org/10.1145/3656650.3656716
SWORD Depositor:	Symplectic Elements
Depositing User:	Symplectic Elements
Date Deposited:	05 Jun 2024 14:54
Last Modified:	05 Jun 2024 15:51
URI:	https://shura.shu.ac.uk/id/eprint/33786

Actions (login required)

View Item

Downloads

Publication Metrics