Co-designing opportunities for human-centred machine learning in supporting type 1 diabetes decision-making

Stawarz, Katarzyna ORCID: https://orcid.org/0000-0001-9021-0615, Katz, Dmitri, Ayobi, Amid, Marshall, Paul, Yamagata, Taku, Santos-Rodriguez, Raul, Flach, Peter and O'Kane, Aisling Ann 2023. Co-designing opportunities for human-centred machine learning in supporting type 1 diabetes decision-making. International Journal of Human-Computer Studies 173 , 103003. 10.1016/j.ijhcs.2023.103003

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Abstract

Type 1 Diabetes (T1D) self-management requires hundreds of daily decisions. Diabetes technologies that use machine learning have significant potential to simplify this process and provide better decision support, but often rely on cumbersome data logging and cognitively demanding reflection on collected data. We set out to use co-design to identify opportunities for machine learning to support diabetes self-management in everyday settings. However, over nine months of interviews and design workshops with 15 people with T1D, we had to re-assess our assumptions about user needs. Our participants reported confidence in their personal knowledge and rejected machine learning based decision support when coping with routine situations, but highlighted the need for technological support in the context of unfamiliar or unexpected situations (holidays, illness, etc.). However, these are the situations where prior data are often lacking and drawing data-driven conclusions is challenging. Reflecting this challenge, we provide suggestions on how machine learning and other artificial intelligence approaches, e.g., expert systems, could enable decision-making support in both routine and unexpected situations.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	Elsevier
ISSN:	1071-5819
Funders:	Innovate UK Digital Catalyst Award - Digital Health, UKRI Turing AI Fellowship EP/V024817/1
Date of First Compliant Deposit:	3 February 2023
Date of Acceptance:	2 February 2023
Last Modified:	05 May 2023 22:32
URI:	https://orca.cardiff.ac.uk/id/eprint/156493

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