Computational Notebooks as Co-Design Tools: Engaging Young Adults Living with Diabetes, Family Carers, and Clinicians with Machine Learning Models

2023. Amid Ayobi, Jacob Hughes, Christopher Duckworth, Jakub Dylag, Sam James, Paul Marshall, Matthew Guy, Anitha Kumaran, Adriane Chapman, Michael Boniface & Aisling O'Kane
Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems.

Abstract

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 co-design 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.

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Citation

Ayobi, A., Hughes, J., Duckworth, C. J., Dylag, J. J., James, S., Marshall, P., … O'Kane, A. A. (2023). Computational notebooks as co-design tools: engaging young adults living with diabetes, family carers, and clinicians with machine learning models. Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. New York, NY, USA: Association for Computing Machinery. URL: https://doi.org/10.1145/3544548.3581424, doi:10.1145/3544548.3581424

BibTeX

@inproceedings{10.1145/3544548.3581424,
author = {Ayobi, Amid and Hughes, Jacob and Duckworth, Christopher J and Dylag, Jakub J and James, Sam and Marshall, Paul and Guy, Matthew and Kumaran, Anitha and Chapman, Adriane and Boniface, Michael and O'Kane, Aisling Ann},
title = {Computational Notebooks as Co-Design Tools: Engaging Young Adults Living with Diabetes, Family Carers, and Clinicians with Machine Learning Models},
year = {2023},
isbn = {9781450394215},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3544548.3581424},
doi = {10.1145/3544548.3581424},
abstract = {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 co-design 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.},
booktitle = {Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems},
articleno = {300},
numpages = {20},
keywords = {Co-Design, Diabetes, Human-AI Interaction, Machine Learning},
location = {Hamburg, Germany},
series = {CHI '23}
}