Engaging end user groups with machine learning (ML) models can help align the design of predictive systems with
people’s needs and expectations. We present a co-design study investigating the benefits and challenges of using
computational notebooks to inform ML models with end user groups. We used a computational notebook to engage
young adults, carers, and clinicians with an example ML model that predicted health risk in diabetes care. Through codesign workshops and retrospective interviews, we found that participants particularly valued using the interactive
data visualisations of the computational notebook to scaffold multidisciplinary learning, anticipate benefits and harms
of the example ML model, and create fictional feature importance plots to highlight care needs. Participants also
reported challenges, from running code cells to managing information asymmetries and power imbalances. We discuss
the potential of leveraging computational notebooks as interactive co-design tools to meet end user needs early in ML
model lifecycles
