Computational Notebooks as Co-Design Tools: Engaging Young Adults Living with Diabetes, Family Carers, and Clinicians with Machine Learning Models
Open accessAbstract
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.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Ayobi et al.'s use of computational notebooks for healthcare co-design involves multimodal interaction, co-operative agency, and relies on guided input and transparency mechanisms.
“Similarly, Ayobi et al.'s use of computational notebooks for healthcare co-design [11] involves multimodal interaction, co-operative agency, and relies on guided input and transparency mechanisms within the notebook structure.”
From Exploring Collaboration Patterns and Strategies in Human-AI Co-creation through the Lens of Agency: A Scoping Review of the Top-tier HCI Literature (2025) · cites this paper as [11]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Using thematic analysis in psychology
Braun, V., & Clarke, V. (2006) · Qualitative Research in Psychology · doi:10.1191/1478088706qp063oa · Cited by 194,414 · see citing works
XGBoost: A Scalable Tree Boosting System
Chen, T., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939785 · Cited by 54,569 · see citing works
Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362 · see citing works
Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems
Mark, G., Fussell, S. R., Lampe, C., Schraefel, M., Hourcade, J., Appert, C., & Wigdor, D. J. (2017) · International Conference on Human Factors in Computing Systems · doi:10.1145/3025453 · Cited by 2,222 · see citing works
Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
(2020) · doi:10.1145/3313831 · Cited by 1,951 · see citing works
(2007) · International Linear Collider · Cited by 1,382 · see citing works
Luger, E., & Sellen, A. (2016) · Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems · doi:10.1145/2858036.2858288 · Cited by 1,176 · see citing works
Participatory Design: Issues and Concerns
Kensing, F., & Blomberg, J. (1998) · Computer Supported Cooperative Work (CSCW) · doi:10.1023/a:1008689307411 · Cited by 1,094 · see citing works
A comprehensive review on automation in agriculture using artificial intelligence
Jha, K., Doshi, A., Patel, P., & Shah, M. (2019) · Artificial Intelligence in Agriculture · doi:10.1016/j.aiia.2019.05.004 · Cited by 1,011 · see citing works
“Everyone wants to do the model work, not the data work”: Data Cascades in High-Stakes AI
Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P., & Aroyo, L. M. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445518 · Cited by 781 · see citing works
Abdul, A., Vermeulen, J., Wang, D., Lim, B. Y., & Kankanhalli, M. (2018) · Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3174156 · Cited by 743 · see citing works
Empowerment and Self-Management of Diabetes
Funnell, M. M., & Anderson, R. M. (2004) · Clinical Diabetes · doi:10.2337/diaclin.22.3.123 · Cited by 731 · see citing works
Recent advances: Consumer health informatics
Eysenbach, G. (2000) · BMJ · doi:10.1136/bmj.320.7251.1713 · Cited by 670 · see citing works
Re-examining Whether, Why, and How Human-AI Interaction Is Uniquely Difficult to Design
Yang, Q., Steinfeld, A., Rosé, C., & Zimmerman, J. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376301 · Cited by 622 · cited 2× · see citing works
“However, designing human-AI interactions can bring about significant challenges [30, 31, 39, 94, 95], such as understanding data science jargon and appreciating ML capabilities [94], envisioning feasible AI experiences and rapidly prototyping realistic human-AI interactions [12, 31, 95], as well as developing a shared…”
BackgroundCai, C. J., Winter, S., Steiner, D., Wilcox, L., & Terry, M. (2019) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3359206 · Cited by 594 · see citing works
Kaur, H., Nori, H., Jenkins, S., Caruana, R., Wallach, H., & Wortman Vaughan, J. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376219 · Cited by 564 · see citing works
Co-Design as a Process of Joint Inquiry and Imagination
Steen, M. (2013) · Design Issues · doi:10.1162/desi_a_00207 · Cited by 526 · see citing works
Madaio, M. A., Stark, L., Wortman Vaughan, J., & Wallach, H. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376445 · Cited by 436 · see citing works
Thieme, A., Belgrave, D., & Doherty, G. (2020) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3398069 · Cited by 406 · see citing works
UX Design Innovation: Challenges for Working with Machine Learning as a Design Material
Dove, G., Halskov, K., Forlizzi, J., & Zimmerman, J. (2017) · Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3025453.3025739 · Cited by 391 · see citing works
Why Jupyter is data scientists’ computational notebook of choice
Perkel, J. M. (2018) · Nature · doi:10.1038/d41586-018-07196-1 · Cited by 382 · see citing works
MAHI: investigation of social scaffolding for reflective thinking in diabetes management
Mamykina, L., Mynatt, E., Davidson, P., & Greenblatt, D. (2008) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/1357054.1357131 · Cited by 355 · see citing works
Exploration and Explanation in Computational Notebooks
Rule, A., Tabard, A., & Hollan, J. D. (2018) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3173606 · Cited by 347 · see citing works
CHI Conference on Human Factors in Computing Systems
Barbosa, S. D. J., Lampe, C. A. C., Appert, C., Shamma, D. A., Drucker, S. M., Williamson, J., & Yatani, K. (2022) · doi:10.1145/3491102 · Cited by 309 · see citing works
50 Years of Test (Un)fairness: Lessons for Machine Learning
Hutchinson, B., & Mitchell, M. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287600 · Cited by 297 · see citing works
Accurate Diabetes Risk Stratification Using Machine Learning: Role of Missing Value and Outliers
Maniruzzaman, M., Rahman, M. J., Al-MehediHasan, M., Suri, H. S., Abedin, M. M., El-Baz, A., & Suri, J. S. (2018) · Journal of Medical Systems · doi:10.1007/s10916-018-0940-7 · Cited by 275 · see citing works
How do Data Science Workers Collaborate? Roles, Workflows, and Tools
Zhang, A. X., Muller, M. J., & Wang, D. (2020) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3392826 · Cited by 268 · see citing works
Understanding artificial intelligence ethics and safetyPreprint
Leslie, D. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.05684 · Cited by 268 · see citing works
How Data Science Workers Work with Data
Muller, M. J., Lange, I., Wang, D., Piorkowski, D., Tsay, J., Liao, Q. V., Dugan, C., & Erickson, T. D. (2019) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300356 · Cited by 262 · see citing works
Factors Predictive of Use and of Benefit From Continuous Glucose Monitoring in Type 1 Diabetes
Juvenile Diabetes Research Foundation Continuous Glucose Monitoring Study Group (2009) · Diabetes Care · doi:10.2337/dc09-0889 · Cited by 250 · see citing works
Investigating How Experienced UX Designers Effectively Work with Machine Learning
Yang, Q., Scuito, A., Zimmerman, J., Forlizzi, J., & Steinfeld, A. M. (2018) · Designing Interactive Systems Conference · doi:10.1145/3196709.3196730 · Cited by 230 · see citing works
Crabtree, A., Rouncefield, M., & Tolmie, P. (2012) · Human-computer interaction series · doi:10.1007/978-1-4471-2726-0 · Cited by 225 · see citing works
Design as a mutual learning process between users and designers
Béguin, P. (2003) · Interacting with Computers · doi:10.1016/s0953-5438(03)00060-2 · Cited by 184 · see citing works
Thieme, A., Hanratty, M., Lyons, M., Palacios, J. E., Marques, R. F., Morrison, C. P., & Doherty, G. J. (2022) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3564752 · Cited by 181 · see citing works
Semi-Automated Tracking: A Balanced Approach for Self-Monitoring Applications
Choe, E. K., Abdullah, S., Rabbi, M., Thomaz, E., Epstein, D. A., Cordeiro, F., Kay, M., Abowd, G. D., Choudhury, T., Fogarty, J. A., et al. (2017) · IEEE Pervasive Computing · doi:10.1109/mprv.2017.18 · Cited by 171 · see citing works
What's Wrong with Computational Notebooks? Pain Points, Needs, and Design Opportunities
Chattopadhyay, S., Prasad, I., Henley, A. Z., Sarma, A., & Barik, T. (2020) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376729 · Cited by 166 · see citing works
Managing Messes in Computational Notebooks
Head, A., Hohman, F., Barik, T., Drucker, S. M., & DeLine, R. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300500 · Cited by 165 · see citing works
How AI Developers Overcome Communication Challenges in a Multidisciplinary Team: A Case Study
Piorkowski, D., Park, S., Wang, A. Y., Wang, D., Muller, M., & Portnoy, F. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3449205 · Cited by 151 · see citing works
Flexible and Mindful Self-Tracking
Ayobi, A., Sonne, T., Marshall, P., & Cox, A. L. (2018) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3173602 · Cited by 149 · see citing works
Self-citationPanigutti, C., Beretta, A., Giannotti, F., & Pedreschi, D. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3502104 · Cited by 145 · see citing works
Values-led participatory design
Iversen, O. S., Halskov, K., & Leong, T. W. (2012) · CoDesign · doi:10.1080/15710882.2012.672575 · Cited by 143 · see citing works
Considerations for AI fairness for people with disabilities
Trewin, S., Basson, S., Muller, M., Branham, S., Treviranus, J., Gruen, D., Hebert, D., Lyckowski, N., & Manser, E. (2019) · AI Matters · doi:10.1145/3362077.3362086 · Cited by 143 · see citing works
Human-Centred Machine Learning
Gillies, M. F. P., Fiebrink, R. A., Tanaka, A., Garcia, J., Bevilacqua, F. P., Héloir, A., Nunnari, F., Mackay, W. E., Amershi, S., Lee, B., et al. (2016) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/2851581.2856492 · Cited by 138 · see citing works
Risk stratification systems for diabetic foot ulcers: a systematic review
Monteiro‐Soares, M., Boyko, E. J., Ribeiro, J. L. D. C., Ribeiro, I., & Dinis-Ribeiro, M. J. (2011) · Diabetologia · doi:10.1007/s00125-010-2030-3 · Cited by 132 · see citing works
How Data Scientists Use Computational Notebooks for Real-Time Collaboration
Wang, A. Y., Mittal, A., Brooks, C. A., & Oney, S. (2019) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3359141 · Cited by 131 · see citing works
Dissecting Racial Bias in an Algorithm that Guides Health Decisions for 70 Million People
Obermeyer, Z., & Mullainathan, S. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287593 · Cited by 124 · see citing works
Clark, C. M., Snyder, J. W., Meek, R. L., et al. (2001) · Diabetes Care · doi:10.2337/diacare.24.6.1079 · Cited by 123 · see citing works
Kim, Y.-H., Jeon, J. H., Lee, B., Choe, E. K., & Seo, J. (2017) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3130930 · Cited by 119 · see citing works
Biermann, O. C., Ma, N. F., & Yoon, D. (2022) · Designing Interactive Systems Conference · doi:10.1145/3532106.3533506 · Cited by 116 · see citing works
AADE Guidelines for the Practice of Diabetes Self-Management Education and Training (DSME/T)
American Association of Diabetes Educators (2009) · The Science of Diabetes Self-Management and Care · doi:10.1177/0145721709352436 · Cited by 113 · see citing works
A Review of User-Centered Design for Diabetes-Related Consumer Health Informatics Technologies
LeRouge, C., & Wickramasinghe, N. S. (2013) · Journal of Diabetes Science and Technology · doi:10.1177/193229681300700429 · Cited by 94 · see citing works
Young adults’ management of Type 1 diabetes during life transitions
Rasmussen, B., Ward, G. M., Jenkins, A. J., King, S. J., & Dunning, T. L. (2011) · Journal of Clinical Nursing · doi:10.1111/j.1365-2702.2010.03657.x · Cited by 91 · see citing works
Stapleton, L., Lee, M. H., Qing, D., Wright, M., Chouldechova, A., Holstein, K., Wu, Z. S., & Zhu, H. (2022) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3531146.3533177 · Cited by 86 · see citing works
Wang, A. Y., Wang, D., Drozdal, J., Muller, M. J., Park, S., Weisz, J. D., Liu, X., Wu, L., & Dugan, C. (2022) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3489465 · Cited by 82 · see citing works
Constructing identities through storytelling in diabetes management
Mamykina, L., Miller, A. D., Mynatt, E. D., & Greenblatt, D. (2010) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/1753326.1753507 · Cited by 81 · see citing works
How Experienced Designers of Enterprise Applications Engage AI as a Design Material
Yildirim, N., Kass, A., Tung, T., Upton, C., Costello, D., Giusti, R., Lacin, S., Lovic, S., O'Neill, J. M., Meehan, R. O., et al. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3517491 · Cited by 71 · see citing works
Morrison, C., Cutrell, E., Dhareshwar, A., Doherty, K., Thieme, A., & Taylor, A. (2017) · Proceedings of the 19th International ACM SIGACCESS Conference on Computers and Accessibility · doi:10.1145/3132525.3132530 · Cited by 69 · see citing works
The Role of HCI in the Age of AI
Harper, R. H. R. (2019) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2019.1631527 · Cited by 68 · see citing works
Soliciting Stakeholders’ Fairness Notions in Child Maltreatment Predictive Systems
Cheng, H.-F., Stapleton, L., Wang, R., Bullock, P., Chouldechova, A., Wu, Z. S. S., & Zhu, H. (2021) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445308 · Cited by 64 · see citing works
Monsters, Metaphors, and Machine Learning
Dove, G., & Fayard, A. (2020) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376275 · Cited by 62 · see citing works
Storni, C. (2013) · Personal and Ubiquitous Computing · doi:10.1007/s00779-013-0707-6 · Cited by 61 · see citing works
Hind, M. (2019) · XRDS Crossroads The ACM Magazine for Students · doi:10.1145/3313096 · Cited by 61 · see citing works
Brew‐Sam, N., Chhabra, M., Parkinson, A., Hannan, K., Brown, E., Pedley, L., Brown, K., Wright, K., Pedley, E., Nolan, C. J., et al. (2021) · JMIR Diabetes · doi:10.2196/20973 · Cited by 58 · see citing works
Subramonyam, H., Im, J., Seifert, C. M., & Adar, E. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3517537 · Cited by 57 · see citing works
Zheng, C., Wang, D., Wang, A. Y., & Ma, X. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3517615 · Cited by 54 · see citing works
Donsa, K., Spat, S., Beck, P., Pieber, T. R., & Holzinger, A. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-16226-3_10 · Cited by 50 · see citing works
Katan, S., Grierson, M., & Fiebrink, R. (2015) · Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems · doi:10.1145/2702123.2702474 · Cited by 50 · see citing works
Benefits and Pitfalls of Jupyter Notebooks in the Classroom
Johnson, J. W. (2020) · Conference on Information Technology Education · doi:10.1145/3368308.3415397 · Cited by 45 · see citing works
Overcoming Barriers to Adoption of Digital Health Tools for Diabetes
Klonoff, D. C., & Kerr, D. E. (2017) · Journal of Diabetes Science and Technology · doi:10.1177/1932296817732459 · Cited by 44 · see citing works
Exploring the Value of Parent Tracked Baby Data in Interactions with Healthcare Professionals
van Kollenburg, J., Bogers, S., Rutjes, H., Deckers, E., Frens, J. W., & Hummels, C. (2018) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3173871 · Cited by 44 · see citing works
ToonNote: Improving Communication in Computational Notebooks Using Interactive Data Comics
Kang, D., Ho, T., Marquardt, N., Mutlu, B., & Bianchi, A. (2021) · International Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445434 · Cited by 44 · see citing works
Take it personally: accounting for individual difference in designing diabetes management systems
Chen, Y. (2010) · Proceedings of the 8th ACM Conference on Designing Interactive Systems · doi:10.1145/1858171.1858218 · Cited by 43 · see citing works
An Uncommon Task: Participatory Design in Legal AI
Delgado, F. A., Barocas, S., & Levy, K. E. C. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3512898 · Cited by 41 · see citing works
Ayobi, A., Stawarz, K., Katz, D., Marshall, P., Yamagata, T., Santos‐Rodríguez, R., Flach, P. A., & O’Kane, A. A. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3479601 · Cited by 40 · see citing works
Self-citationDoryab, A., Frost, M. M., Faurholt‐Jepsen, M., Kessing, L. V., & Bardram, J. E. (2014) · Personal and Ubiquitous Computing · doi:10.1007/s00779-014-0826-8 · Cited by 38 · see citing works
Co-designing AI Futures: Integrating AI Ethics, Social Computing, and Design
Loi, D., Wolf, C. T., Blomberg, J. L., Arar, R., & Brereton, M. (2019) · Companion Publication of the 2019 on Designing Interactive Systems Conference 2019 Companion · doi:10.1145/3301019.3320000 · Cited by 38 · see citing works
Kross, S., & Guo, P. J. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3476052 · Cited by 38 · see citing works
Research on human–AI co-creation based on reflective design practice
Fu, Z., & Zhou, Y. (2020) · CCF Transactions on Pervasive Computing and Interaction · doi:10.1007/s42486-020-00028-0 · Cited by 29 · see citing works
Loi, D., Lodato, T. J., Wolf, C. T., Arar, R., & Blomberg, J. (2018) · Participatory Design Conference · doi:10.1145/3210604.3210614 · Cited by 28 · see citing works
Eliciting Best Practices for Collaboration with Computational Notebooks
Quaranta, L., Calefato, F., & Lanubile, F. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3512934 · Cited by 28 · see citing works
Walsh, G., & Wronsky, E. (2019) · ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW) · doi:10.1145/3311957.3359456 · Cited by 27 · see citing works
Stakeholder Participation in AI: Beyond "Add Diverse Stakeholders and Stir"Preprint
Delgado, F. A., Yang, S. J. H., Madaio, M., & Yang, Q. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.01122 · Cited by 24 · see citing works
Breaking up data-enabled design: expanding and scaling up for the clinical context
Noortman, R., Lövei, P., Funk, M., Deckers, E., Wensveen, S. S. A. G., & Eggen, B. H. (2022) · Artificial intelligence for engineering design analysis and manufacturing · doi:10.1017/s0890060421000433 · Cited by 23 · see citing works
Chang, B. P. I., Webb, T. L., & Benn, Y. (2017) · Frontiers in Psychology · doi:10.3389/fpsyg.2017.00152 · Cited by 22 · see citing works
Developing Students' Written Communication Skills with Jupyter Notebooks
Willis, A., Charlton, P., & Hirst, T. (2020) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/3328778.3366927 · Cited by 21 · see citing works
Interactive Machine Learning for End-User Innovation
Bernardo, F., Zbyszyński, M., Fiebrink, R. A., & Grierson, M. (2016) · Goldsmiths (University of London) · Cited by 20 · see citing works
Doherty, S. T., & Greaves, S. P. (2015) · Journal of Diabetes Research · doi:10.1155/2015/804341 · Cited by 20 · see citing works
State of Type 1 Diabetes Care in the United States in 2016–2018 from T1D Exchange Registry Data
Rodbard, D. (2019) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2019.0008 · Cited by 18 · see citing works
Artificially Intelligent Technology for the Margins: A Multidisciplinary Design Agenda
Tachtler, F. M., Aal, K., Ertl, T., Diethei, D., Niess, J., Khwaja, M. K., Talhouk, R., Vilaza, G. N., Lazem, S., Singh, A., et al. (2021) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3411763.3441333 · Cited by 7 · see citing works
Better Supporting Workers in ML Workplaces
Clarke, M. F., Gonzales, J., Harper, R. H. R., Randall, D. W., Ludwig, T. G., & Ikeya, N. (2019) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/3311957.3359429 · Cited by 6 · see citing works
Introduction to Data-Enabled Design
Lövei, P., Noortman, R., & Funk, M. (2022) · CHI Conference on Human Factors in Computing Systems Extended Abstracts · doi:10.1145/3491101.3503749 · Cited by 5 · see citing works
Flagged first, then by citations
Research works printed but not linked: 7
- [4]4. Amid Ayobi, Katarzyna Stawarz, Dmitri Katz, Paul Marshall, Taku Yamagata, Raúl Santos-Rodríguez, Peter Flach, and Aisling Ann O'Kane. 2021. Machine Learning Explanations as Boundary Objects: How AI Researchers Explain and Non-Experts Perceive Machine Learning. Joint Proceedings of the ACM IUI 2021 Workshops, April 13-17, 2021, College Station, USA. DOI:http://ceur-ws.org/Vol- 2903/IUI21WS-TExSS-3.pdf
- [14]14. Mirian Calvo, Leon Cruickshank, and Madeleine Sclater. 2022. Exploring mutual learning in co-design. DISCERN: International Journal of Design for Social Change, Sustainable Innovation and Entrepreneurship 3, 2 (November 2022), 79-96.
- [24]24. A. Crabtree, M. Rouncefield, and P. Tolmie. 2012. Doing Design Ethnogra- phy. Springer London. Retrieved from https://books.google.co.uk/books?id$= $Irm2KKegDjQC
- [34]34. Neil Fleming and David Baume. 2006. Learning Styles Again: VARKing up the right tree! Educational developments 7, 4 (2006), 4.
- [35]35. Neil D. Fleming. 1995. I'm different; not dumb. Modes of presentation (VARK) in the tertiary classroom. In Research and development in higher education, Pro- ceedings of the 1995 Annual Conference of the Higher Education and Research Development Society of Australasia (HERDSA), HERDSA, 308-313.
- [39]39. Fabien Girardin and Neal Lathia. 2017. When User Experience Designers Partner with Data Scientists. In AAAI Spring Symposia.
- [73]73. Aisling Ann O'Kane, Yvonne Rogers, and Ann E. Blandford. 2015. Concealing or Revealing Mobile Medical Devices?: Designing for Onstage and Offstage Presentation. In Proceedings of the 33rd annual ACM conference on human factors in computing systems, ACM, 1689-1698.
Cited by
Sorted by citations
Zhang, S., Wang, H., & Yi, X. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3757594 · Cited by 36 · see citing works
“Human agency is often linked to notions of intentionality, self-causality [17], and the experience of control over one’s actions and their effects, sometimes conceptualized via ‘locus of control’ [157].”
Influential
- Authors
- Ayobi, A., Hughes, J., Duckworth, C. J., Dylag, J. J., Gordon James, S., Marshall, P., Guy, M., Kumaran, A., Chapman, A., Boniface, M., & O'Kane, A. A.
- Affiliations
- University College London (GB)
- University of Bristol (GB)
- University of Southampton (GB)
- University of Bristol, United Kingdom
- University Hospital Southampton NHS Foundation Trust (GB)
- ORCID
- Venue
- Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2023-04-19
- Pages
- 1-20
- OpenAlex
- W4366590502
- Semantic Scholar
- 2a2c44b918f1dd0a4d3f6d42b2cbc00334a0dc2d
- Cited by
- 26
- References
- 94
- Citations by year
- 2026: 4 · 2025: 13 · 2024: 8 · 2023: 1
- FWCI
- 4.61
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- ePrints Soton (University of Southampton) (acceptedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Bristol Research (University of Bristol) (submittedVersion, other-oa)
- Explore Bristol Research (submittedVersion, other-oa)
- Funders
- UK Research and Innovation (RITM0372366)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- computational notebooks, co-design, machine learning models, diabetes management, young adults with diabetes, family caregivers, clinicians, end-user involvement, health risk prediction, interactive data visualization, feature importance, multidisciplinary learning, power imbalance, information asymmetry
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
