Projects per year
Abstract
Understanding artificial intelligence (AI) and machine learning (ML) approaches is becoming increasingly important for people with a wide range of professional backgrounds. However, it is unclear how ML concepts can be effectively explained as part of human-centred and multidisciplinary design processes. We provide a qualitative account of how AI researchers explained and non-experts perceived ML concepts as part of a co-design project that aimed to inform the design of ML applications for diabetes self-care. We identify benefits and challenges of explaining ML concepts with analogical narratives, information visualisations, and publicly available videos. Co-design participants reported not only gaining an improved understanding of ML concepts but also highlighted challenges of understanding ML explanations, including misalignments between scientific models and their lived self-care experiences and individual information needs. We frame our findings through the lens of Stars and Griesemer’s concept of boundary objects to discuss how the presentation of user-centred ML explanations could strike a balance between being plastic and robust enough to support design objectives and people’s individual information needs.
Original language | English |
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Title of host publication | Joint Proceedings of the ACM IUI 2021 Workshops |
Publisher | CEUR Workshop Proceedings |
Volume | 2903 |
Publication status | Published - 17 Apr 2021 |
Event | Workshop on Transparency and Explanations in Smart Systems (TEXSS) - Duration: 13 Apr 2021 → 13 Apr 2021 https://explainablesystems.comp.nus.edu.sg/2021/ |
Publication series
Name | CEUR workshop proceedings |
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ISSN (Print) | 1613-0073 |
Name | Central EURope workshop proceedings |
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ISSN (Print) | 1613-0073 |
Workshop
Workshop | Workshop on Transparency and Explanations in Smart Systems (TEXSS) |
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Period | 13/04/21 → 13/04/21 |
Internet address |
Research Groups and Themes
- Bristol Interaction Group
Fingerprint
Dive into the research topics of 'Machine Learning Explanations as Boundary Objects: How AI Researchers Explain and Non-Experts Perceive Machine Learning'. Together they form a unique fingerprint.Projects
- 1 Finished
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ML4D: InnovateUK ML4D: Machine Learning for Enhanced Diabetes Self-Care
O'Kane, A. A. (Principal Investigator), Marshall, P. (Co-Investigator), Santos-Rodriguez, R. (Co-Investigator) & Flach, P. A. (Co-Investigator)
1/11/18 → 30/04/20
Project: Research
Student theses
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Towards Safe and Robust Reinforcement Learning: Leveraging Multiple Sources of Information
Yamagata, T. (Author), Santos-Rodriguez, R. (Supervisor), 10 Dec 2024Student thesis: Doctoral Thesis › Doctor of Philosophy (PhD)
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