Co-designing opportunities for Human-Centred Machine Learning in supporting Type 1 diabetes decision-making. Issue 173 (May 2023) - Stawarz, Katarzyna; Katz, Dmitri; Ayobi, Amid; Marshall, Paul; Yamagata, Taku; Santos-Rodriguez, Raul; Flach, Peter; O'Kane, Aisling Ann
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. Highlights: Diabetes decision support systems must address different types of situations. Every type of situation (routine, unexpected, etc.) requires a different approach. Decision support is most valued in situations where personal heuristics are lacking. Future systems require a combination of machine learning and other AI approaches.