The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Explainability via Interactivity? Supporting Nonexperts’ Sensemaking of pre-trained CNN by Interacting with Their Daily Surroundings
Open accessAbstract
Current research on Explainable AI (XAI) heavily targets on expert users (data scientists or AI developers). However, increasing importance has been argued for making AI more understandable to nonexperts, who are expected to leverage AI techniques, but have limited knowledge about AI. We present a mobile application to support nonexperts to interactively make sense of Convolutional Neural Networks (CNN); it allows users to play with a pre-trained CNN by taking pictures of their surrounding objects. We use an up-to-date XAI technique (Class Activation Map) to intuitively visualize the model’s decision (the most important image regions that lead to a certain result). Deployed in a university course, this playful learning tool was found to support design students to gain vivid understandings about the capabilities and limitations of pre-trained CNNs in real-world environments. Concrete examples of students’ playful explorations are reported to characterize their sensemaking processes reflecting different depths of thought.
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“Interactive healthcare simulations of this nature would enhance training of healthcare professionals (Wang and An 2021), particularly for rare or high risk events and difficult to access personnel.”
From Human-Clinical AI Agent Collaboration (2025) · cites this paper as Wang and An 2021
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Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., et al. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.12.012 · Cited by 10,423
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 1×
“Our approach focuses on such non-experts (namely designers), who are expected to benefit from utilizing pre-trained AI model in their domain-specific tasks [18][19][5], but only have limited knowledge about AI.”
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Research works printed but not linked: 18
- Martín Abadi , Paul Barham , Jianmin Chen , Zhifeng Chen , Andy Davis , Jeffrey Dean , Matthieu Devin , Sanjay Ghemawat , Geoffrey Irving , and Michael Isard . 2016 . Tensorflow: A system for large-scale machine learning . In 12th USENIX symposium on operating systems design and implementation (OSDI 16) . 265–283. Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, and Michael Isard. 2016. Tensorflow: A system for large-scale machine learning. In 12th USENIX symposium on operating systems design and implementation (OSDI 16). 265–283.
- 10.1145/3334480.3382839 · DOI
- 10.1145/3025453.3025739 · DOI · Paper page
- Riccardo Guidotti , Anna Monreale , Salvatore Ruggieri , Franco Turini , Dino Pedreschi , and Fosca Giannotti . 2018. A survey of methods for explaining black box models. arXiv 51, 5 ( 2018 ), 1–42. arxiv:1802.01933 Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Franco Turini, Dino Pedreschi, and Fosca Giannotti. 2018. A survey of methods for explaining black box models. arXiv 51, 5 (2018), 1–42. arxiv:1802.01933 · arXiv
- 10.1145/2647868.2654889 · DOI · Paper page
- Min Lin , Qiang Chen , and Shuicheng Yan . 2014 . Network in network . 2nd International Conference on Learning Representations, ICLR 2014 - Conference Track Proceedings(2014) . arxiv:1312.4400 Min Lin, Qiang Chen, and Shuicheng Yan. 2014. Network in network. 2nd International Conference on Learning Representations, ICLR 2014 - Conference Track Proceedings(2014). arxiv:1312.4400 · arXiv
- Drew Linsley , Dan Shiebler , Sven Eberhardt , and Thomas Serre . 2019 . Learning what and where to attend . 7th International Conference on Learning Representations, ICLR 2019 (2019 ), 1–21. arxiv:1805.08819 Drew Linsley, Dan Shiebler, Sven Eberhardt, and Thomas Serre. 2019. Learning what and where to attend. 7th International Conference on Learning Representations, ICLR 2019 (2019), 1–21. arxiv:1805.08819 · arXiv
- 10.1145/3411764.3445096 · DOI
- Donald A Schon. 1984. The reflective practitioner: How professionals think in action. Vol. 5126. Basic books. Donald A Schon. 1984. The reflective practitioner: How professionals think in action. Vol. 5126. Basic books.
- 10.1007/s11263-019-01228-7 · DOI · Paper page
- Karen Simonyan and Andrew Zisserman . 2015 . Very deep convolutional networks for large-scale image recognition . 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings(2015) . arxiv:1409.1556 Karen Simonyan and Andrew Zisserman. 2015. Very deep convolutional networks for large-scale image recognition. 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings(2015). arxiv:1409.1556 · arXiv
- Daniel Smilkov Nikhil Thorat Yannick Assogba Ann Yuan Nick Kreeger Ping Yu Kangyi Zhang Shanqing Cai Eric Nielsen and David Soergel. 2019. Tensorflow. js: Machine learning for the web and beyond. arXiv preprint arXiv:1901.05350(2019). Daniel Smilkov Nikhil Thorat Yannick Assogba Ann Yuan Nick Kreeger Ping Yu Kangyi Zhang Shanqing Cai Eric Nielsen and David Soergel. 2019. Tensorflow. js: Machine learning for the web and beyond. arXiv preprint arXiv:1901.05350(2019). · arXiv
- Daniel Smilkov Nikhil Thorat Charles Nicholson Emily Reif Fernanda B Viégas and Martin Wattenberg. 2016. Embedding projector: Interactive visualization and interpretation of embeddings. arXiv preprint arXiv:1611.05469(2016). Daniel Smilkov Nikhil Thorat Charles Nicholson Emily Reif Fernanda B Viégas and Martin Wattenberg. 2016. Embedding projector: Interactive visualization and interpretation of embeddings. arXiv preprint arXiv:1611.05469(2016). · arXiv
- 10.1145/3196709.3196730 · DOI
- 10.1145/3196709.3196729 · DOI · Paper page
- 10.1109/CVPR.2010.5539957 · DOI
- 10.1109/ICCV.2011.6126474 · DOI
- 10.1109/CVPR.2016.319 · DOI · Paper page
Cited by
Sorted by citations
Human-Clinical AI Agent Collaboration
Kadem, M., & Al-Khazraji, B. (2025) · Proceedings of the AAAI Symposium Series · doi:10.1609/aaaiss.v6i1.36060
Metadata
- Authors
- Wang, C., & An, P.
- Affiliations
- Honda (Germany) (DE)
- University of Waterloo (CA)
- Venue
- Extended Abstracts of the 2021 Annual Symposium on Computer-Human Interaction in Play
- Publisher
- ACM
- Published
- 2021-10-15
- Pages
- 274-279
- OpenAlex
- W3180797935
- Cited by
- 6
- References
- 2
- Citations by year
- 2025: 2 · 2024: 3 · 2023: 1
- FWCI
- 0.45
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Annual Symposium on Computer-Human Interaction in Play (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Data Visualization and Analytics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Visual and Cognitive Learning Processes
- Keywords
- explainable AI, non-expert users, sensemaking, convolutional neural networks, pre-trained convolutional neural networks, class activation maps, interactive visualization, mobile application, playful learning, design students, AI literacy
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- Wang, C., & An, P. (2021). Explainability via Interactivity? Supporting Nonexperts’ Sensemaking of pre-trained CNN by Interacting with Their Daily Surroundings. Extended Abstracts of the 2021 Annual Symposium on Computer-Human Interaction in Play. https://doi.org/10.1145/3450337.3483487
