Machine Learning Will Save the Day : Lessons from Engaging Experts in High-Stakes Visualization Projects
Sopasakis, Alexandros LU
(2025)
2025 IEEE Conference on Engaging Critical Workforce In co-Design aNd Assessment, ECWIDNA 2025
In Proceedings - 2025 IEEE Conference on Engaging Critical Workforce In co-Design aNd Assessment, ECWIDNA 2025
p.23-25
- Abstract
Collaborating with highly skilled professionals in participatory design of machine learning (ML) and visualization tools presents unique challenges. This paper reflects on interdisciplinary projects involving medicine, biology, and the social sciences, where ML was introduced to support complex data analysis and segmentation tasks, sometimes from the outset and other times as a later addition. Despite initial enthusiasm, many efforts were hindered by unrealistic expectations, unclear roles, limited stakeholder availability, and evolving project scopes. Through these experiences, we identify recurring barriers to successful end-user development, including overestimation of ML capabilities and fragile team structures. We propose practical... (More)
Collaborating with highly skilled professionals in participatory design of machine learning (ML) and visualization tools presents unique challenges. This paper reflects on interdisciplinary projects involving medicine, biology, and the social sciences, where ML was introduced to support complex data analysis and segmentation tasks, sometimes from the outset and other times as a later addition. Despite initial enthusiasm, many efforts were hindered by unrealistic expectations, unclear roles, limited stakeholder availability, and evolving project scopes. Through these experiences, we identify recurring barriers to successful end-user development, including overestimation of ML capabilities and fragile team structures. We propose practical strategies such as lightweight review rituals and structured documentation to improve resilience and alignment in co-design workflows. These insights contribute to ongoing conversations about effective collaboration and sustainable design practices with critical workforce participants.
(Less)
- author
- Sopasakis, Alexandros
LU
- organization
- publishing date
- 2025
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- co-design, domain expert collaboration, interdisciplinary, machine learning, Participatory design, visualization
- host publication
- Proceedings - 2025 IEEE Conference on Engaging Critical Workforce In co-Design aNd Assessment, ECWIDNA 2025
- series title
- Proceedings - 2025 IEEE Conference on Engaging Critical Workforce In co-Design aNd Assessment, ECWIDNA 2025
- pages
- 3 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 2025 IEEE Conference on Engaging Critical Workforce In co-Design aNd Assessment, ECWIDNA 2025
- conference location
- Vienna, Austria
- conference dates
- 2025-11-03 - 2025-11-03
- external identifiers
-
- scopus:105032630199
- ISBN
- 9798331590062
- DOI
- 10.1109/ECWIDNA68506.2025.00011
- language
- English
- LU publication?
- yes
- additional info
- Publisher Copyright: © 2025 IEEE.
- id
- df5a92b8-54f0-4508-93fa-e8a07f9b8a50
- date added to LUP
- 2026-03-19 05:58:18
- date last changed
- 2026-06-29 10:37:31
@inproceedings{df5a92b8-54f0-4508-93fa-e8a07f9b8a50,
abstract = {{<p>Collaborating with highly skilled professionals in participatory design of machine learning (ML) and visualization tools presents unique challenges. This paper reflects on interdisciplinary projects involving medicine, biology, and the social sciences, where ML was introduced to support complex data analysis and segmentation tasks, sometimes from the outset and other times as a later addition. Despite initial enthusiasm, many efforts were hindered by unrealistic expectations, unclear roles, limited stakeholder availability, and evolving project scopes. Through these experiences, we identify recurring barriers to successful end-user development, including overestimation of ML capabilities and fragile team structures. We propose practical strategies such as lightweight review rituals and structured documentation to improve resilience and alignment in co-design workflows. These insights contribute to ongoing conversations about effective collaboration and sustainable design practices with critical workforce participants.</p>}},
author = {{Sopasakis, Alexandros}},
booktitle = {{Proceedings - 2025 IEEE Conference on Engaging Critical Workforce In co-Design aNd Assessment, ECWIDNA 2025}},
isbn = {{9798331590062}},
keywords = {{co-design; domain expert collaboration; interdisciplinary; machine learning; Participatory design; visualization}},
language = {{eng}},
pages = {{23--25}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
series = {{Proceedings - 2025 IEEE Conference on Engaging Critical Workforce In co-Design aNd Assessment, ECWIDNA 2025}},
title = {{Machine Learning Will Save the Day : Lessons from Engaging Experts in High-Stakes Visualization Projects}},
url = {{http://dx.doi.org/10.1109/ECWIDNA68506.2025.00011}},
doi = {{10.1109/ECWIDNA68506.2025.00011}},
year = {{2025}},
}