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User Insights shaping Machine Learning applied to Archives

Authors:

Surya Kasturi,

Alex Shenfield,

Christopher RoastAuthors Info & Claims

AVI '24: Proceedings of the 2024 International Conference on Advanced Visual Interfaces

Article No.: 67, Pages 1 - 3

https://doi.org/10.1145/3656650.3656716

Published: 03 June 2024 Publication History

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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.

References

[1]

Giovanni Colavizza, Tobias Blanke, Charles Jeurgens, and Julia Noordegraaf. 2021. Archives and AI: An overview of current debates and future perspectives. ACM Journal on Computing and Cultural Heritage (JOCCH) 15, 1 (2021), 1–15.

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[2]

Michael DeBellis and Christine Haapala. 1995. User-centric software engineering. IEEE Expert 10, 1 (1995), 34–41.

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[3]

Surya Kasturi, Alex Shenfield, Chris Roast, Danny Le Page, and Alice Broome. 2023. Object Detection in Heritage Archives Using a Human-in-Loop Concept. In UK Workshop on Computational Intelligence. Springer, 170–181.

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[4]

Donald A Norman. 1998. The invisible computer: why good products can fail, the personal computer is so complex, and information appliances are the solution. MIT press.

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[5]

Jane Stevenson. 2022. Machine Learning with Archive Collections. https://blog.archiveshub.jisc.ac.uk/2022/02/28/machine-learning-with-archive-collections/ Accessed on 2024-03-07.

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Index Terms

User Insights shaping Machine Learning applied to Archives
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI theory, concepts and models
  2. Interaction design
    1. Interaction design process and methods

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Published In

AVI '24: Proceedings of the 2024 International Conference on Advanced Visual Interfaces

June 2024

578 pages

ISBN:9798400717642

DOI:10.1145/3656650

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 03 June 2024

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AVI 2024

AVI 2024: International Conference on Advanced Visual Interfaces 2024

June 3 - 7, 2024

Arenzano, Genoa, Italy

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AVI '24 Paper Acceptance Rate 21 of 82 submissions, 26%;

Overall Acceptance Rate 128 of 490 submissions, 26%

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Abstract

References

Index Terms

Recommendations

Are Users of Digital Archives Ready for the AI Era? Obstacles to the Application of Computational Research Methods and New Opportunities

Archives, Quo Vadis et Cum Quibus?

Toward harnessing user feedback for machine learning

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