[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128499-en":3,"doc-seo-128499-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128499,687207017582,"Himbo","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Computational Notebooks as Co-Design Tools - Engaging Young Adults Living with Diabetes, Family Carers, and Clinicians with Machine Learning Models","Engaging end user groups with machine learning systems supports aligning predictive design with real needs and expectations. A co-design study examines how computational notebooks can inform diabetes-care ML models for young adults, family carers, and clinicians. Using interactive visualisations and notebook-based explanations, participants scaffold multidisciplinary learning, anticipate benefits and harms, and generate fictional feature-importance plots to surface care needs. Reported challenges include executing code cells and navigating information asymmetries and power imbalances. Findings discuss notebook use as early co-design tools across ML lifecycles.","Computational Notebooks as Co-Design Tools: Engaging Young Adults Living with Diabetes, Family Carers, and Clinicians with Machine Learning Models  \nAmid Ayobi  \nUniversity College London, London, United Kingdom  \nJacob Hughes  \nUniversity of Bristol, Bristol, United Kingdom  \nChristopher J Duckworth  \nUniversity of Southampton, Southampton, United Kingdom  \nJakubJ Dylag  \nUniversity of Southampton, Southampton, United Kingdom  \nSam James  \nUniversity of Bristol, Bristol, United Kingdom  \nPaul Marshall  \nUniversity of Bristol, Bristol, United Kingdom  \nMatthew Guy  \nUniversity Hospital Southampton, Southampton, United Kingdom  \nAnitha Kumaran  \nUniversity Hospital Southampton, Southampton, United Kingdom  \nAdriane Chapman  \nUniversity of Southampton, Southampton, United Kingdom  \nMichael Boniface  \nUniversity of Southampton, Southampton, United Kingdom  \nAisling Ann O'Kane  \nUniversity of Bristol, Bristol, United Kingdom  \n\"© Ayobi et al. 2023. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive version is being published in: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23), April 23–28, 2023, Hamburg, Germany. ACM, New York, NY, USA, 20 pages. [https://doi.org/10.1145/3544548.3581424](https://doi.org/10.1145/3544548.3581424)  \nEngaging 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.  \nCCS CONCEPTS • Human-centered computing~Human computer interaction (HCI)~Empirical studies in HCI Additional Keywords and Phrases: Co-Design, Machine Learning, Human-AI Interaction, Diabetes  \n1 Introduction  \nArtificial intelligence (AI) is increasingly becoming ubiquitous in people’s daily work and life. From healthcare to agriculture [32,45], AI has the potential to transform industry, but may also undermine human values and amplify structural inequalities [62,72,86]. As a response, academic institutions, corporations, and government bodies have focused attention on human-centred principles, such as fairness, accountability, trust, and ethics [ 1,43,58,63] . Research increasingly draws on human-centred and participatory approaches to empower different stakeholders in the design of AI-driven systems [60], including regulatory bodies and end consumers whose lives can be significantly affected by algorithmic outcomes [42] . However, responsible AI requires not only multidisciplinary collaborations but also novel interdisciplinary approaches that bridge applied disciplines, such as data science and user experience design [40,85] .  \nComputational notebooks provide significant potential to facilitate multidisciplinary collaborations aimed at informing the design of human-centred AI algorithms. People from diverse professional backgrounds already use computational notebooks to combine code, text, and multimedia resources in a single document to explore and visualise data [75,80] . Platforms, such as Jupyter Notebook [47], have been shown to be particularly suitable to","cbCainwhgloN4x1w","https://ap.wps.com/l/cbCainwhgloN4x1w","pdf",2836859,3,1,27,"English","en",105,"# Introduction\n## Human-centred and participatory AI design\n## Computational notebooks for multidisciplinary collaboration\n## Co-design study method and participants\n## Benefits of notebook-based ML explanations\n## Challenges and implications for future tools","[{\"question\":\"What is the purpose of using computational notebooks in the study?\",\"answer\":\"To support co-design by combining code, text, interactive ML explanations, and design fiction to help end users engage with an example diabetes-care ML model.\"},{\"question\":\"How did participants benefit from the computational notebook?\",\"answer\":\"They valued interactive data visualisations for multidisciplinary learning, for anticipating potential benefits and harms, and for creating fictional feature-importance plots reflecting care needs.\"},{\"question\":\"What challenges did participants report while using the notebook?\",\"answer\":\"They reported difficulties running code cells and managing information asymmetries and power imbalances among stakeholders.\"}]","Computational Notebooks as Co-Design Tools - 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