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  1. ; ; ; ; ; ; ; ; ; ; et al (, ACM)
    Mueller, Florian Floyd; Kyburz, Penny; Williamson, Julie R; Sas, Corina; Wilson, Max L; Dugas, Phoebe Toups; Shklovski, Irina (Ed.)
    Advances in artificial intelligence (AI) have enabled unprecedented capabilities, yet innovation teams struggle when envisioning AI concepts. Data science teams think of innovations users do not want, while domain experts think of innovations that cannot be built. A lack of effective ideation seems to be a breakdown point. How might multidisciplinary teams identify buildable and desirable use cases? This paper presents a first hand account of ideating AI concepts to improve critical care medicine. As a team of data scientists, clinicians, and HCI researchers, we conducted a series of design workshops to explore more effective approaches to AI concept ideation and problem formulation. We detail our process, the challenges we encountered, and practices and artifacts that proved effective. We discuss the research implications for improved collaboration and stakeholder engagement, and discuss the role HCI might play in reducing the high failure rate experienced in AI innovation. 
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  2. ; ; ; ; ; (, Proceedings of the CHI Conference on Human Factors in Computing Systems)
    Mueller, Florian Floyd; Kyburz, Penny; Williamson, Julie R; Sas, Corina; Wilson, Max L; Dugas, Phoebe Toups; Shklovski, Irina (Ed.)
    Efficient Type 1 Diabetes (T1D) management necessitates comprehensive tracking of various factors that influence blood sugar levels. However, tracking health data for children with T1D poses unique challenges, as it requires the active involvement of both children and their parents. This study aims to uncover the benefits, challenges, and strategies associated with collaborative tracking for children (ages 6-12) with T1D and their parents. Over a three-week data collection probe study with 22 child-parent pairs, we found that collaborative tracking, characterized by the shared responsibility of tracking management and data provision, yielded positive outcomes for both children and their parents. Drawing from these findings, we delineate four distinct tracking approaches: child-independent, child-led, parent-led, and parent-independent. Our study offers insights for designing health technologies that empower both children and parents in learning and encourage the sharing of different perspectives through collaborative tracking. 
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