skip to main content
10.1145/3411763.3451798acmconferencesArticle/Chapter ViewAbstractPublication PageschiConference Proceedingsconference-collections
poster

Human-AI Interactive and Continuous Sensemaking: A Case Study of Image Classification using Scribble Attention Maps

Published: 08 May 2021 Publication History

Abstract

Advances in Artificial Intelligence (AI), especially the stunning achievements of Deep Learning (DL) in recent years, have shown AI/DL models possess remarkable understanding towards the logic reasoning behind the solved tasks. However, human understanding towards what knowledge is captured by deep neural networks is still elementary and this has a detrimental effect on human’s trust in the decisions made by AI systems. Explainable AI (XAI) is a hot topic in both AI and HCI communities in order to open up the blackbox to elucidate the reasoning processes of AI algorithms in such a way that makes sense to humans. However, XAI is only half of human-AI interaction and research on the other half - human’s feedback on AI explanations together with AI making sense of the feedback - is generally lacking. Human cognition is also a blackbox to AI and effective human-AI interaction requires unveiling both blackboxes to each other for mutual sensemaking. The main contribution of this paper is a conceptual framework for supporting effective human-AI interaction, referred to as interactive and continuous sensemaking (HAICS). We further implement this framework in an image classification application using deep Convolutional Neural Network (CNN) classifiers as a browser-based tool that displays network attention maps to the human for explainability and collects human’s feedback in the form of scribble annotations overlaid onto the maps. Experimental results using a real-world dataset has shown significant improvement of classification accuracy (the AI performance) with the HAICS framework.

References

[1]
Saleema Amershi, Maya Cakmak, William Bradley Knox, and Todd Kulesza. 2014. Power to the People: The Role of Humans in Interactive Machine Learning. AI Magazine 35, 4 (2014), 105–120.
[2]
Gagan Bansal, Besmira Nushi, Ece Kamar, Daniel S Weld, Walter S Lasecki, and Eric Horvitz. 2019. Updates in human-ai teams: Understanding and addressing the performance/compatibility tradeoff. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 2429–2437.
[3]
Robert W Beasley. 2003. Beasley’s Surgery of the Hand.Thieme Medical.
[4]
Raymond Bond, Maurice D. Mulvenna, Hui Wan, Dewar D. Finlay, Alexander Wong, Ansgar Koene, Rob Brisk, Jennifer Boger, and Tameem Adel. 2019. Human Centered Artificial Intelligence: Weaving UX into Algorithmic Decision Making. In 16th International Conference on Human-Computer Interaction (RoCHI). 2–9.
[5]
Yuri Y Boykov and M-P Jolly. 2001. Interactive graph cuts for optimal boundary & region segmentation of objects in ND images. In Proceedings eighth IEEE international conference on computer vision, Vol. 1. 105–112.
[6]
Dominik Dellermann, Adrian Calma, Nikolaus Lipusch, Thorsten Weber, Sascha Weigel, and Philipp Ebel. 2019. The Future of Human-AI Collaboration: A Taxonomy of Design Knowledge for Hybrid Intelligence Systems. In Proceedings of the 52nd Hawaii International Conference on System Sciences. 274–283.
[7]
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. 2009. Imagenet: A large-scale hierarchical image database. In IEEE conference on computer vision and pattern recognition. 248–255.
[8]
Jerry Alan Fails and Dan R. Olsen. 2003. Interactive Machine Learning. In Proceedings of the 8th International Conference on Intelligent User Interfaces. 39–45.
[9]
Marco Gillies, Rebecca Fiebrink, Atau Tanaka, Jérémie Garcia, Frédéric Bevilacqua, Alexis Heloir, Fabrizio Nunnari, Wendy Mackay, Saleema Amershi, Bongshin Lee, 2016. Human-centred machine learning. In Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems. 3558–3565.
[10]
Leo Grady. 2006. Random walks for image segmentation. IEEE transactions on pattern analysis and machine intelligence 28, 11(2006), 1768–1783.
[11]
Varun Gulshan, Carsten Rother, Antonio Criminisi, Andrew Blake, and Andrew Zisserman. 2010. Geodesic star convexity for interactive image segmentation. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 3129–3136.
[12]
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition. 770–778.
[13]
Ece Kamar. 2016. Directions in hybrid intelligence: complementing AI systems with human intelligence. In Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI). 4070–4073.
[14]
Diederik P Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. Proceedings of International Conference on Learning Representations (ICLR) (2015).
[15]
Alexander Kolesnikov and Christoph H Lampert. 2016. Seed, expand and constrain: Three principles for weakly-supervised image segmentation. In European conference on computer vision. Springer, 695–711.
[16]
Todd Kulesza, Margaret Burnett, Wengkeen Wong, and Simone Stumpf. 2015. Principles of Explanatory Debugging to Personalize Interactive Machine Learning. In Proceedings of the 20th International Conference on Intelligent User Interfaces. 126–137.
[17]
Vivian Lai, Samuel Carton, and Chenhao Tan. 2020. Harnessing Explanations to Bridge AI and Humans. In Proceedings of CHI 2020 Fair & Responsible AI Workshop. 1–4.
[18]
David WG Langerhuizen, Anne Eva J Bulstra, Stein J Janssen, David Ring, Gino MMJ Kerkhoffs, Ruurd L Jaarsma, and Job N Doornberg. 2020. Is Deep Learning On Par with Human Observers for Detection of Radiographically Visible and Occult Fractures of the Scaphoid?Clinical Orthopaedics and Related Research(2020).
[19]
Yin Li, Jian Sun, Chi-Keung Tang, and Heung-Yeung Shum. 2004. Lazy snapping. ACM Transactions on Graphics (ToG) 23, 3 (2004), 303–308.
[20]
Di Lin, Jifeng Dai, Jiaya Jia, Kaiming He, and Jian Sun. 2016. Scribblesup: Scribble-supervised convolutional networks for semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 3159–3167.
[21]
Chris J Michael, Dina Acklin, and Jaelle Scheuerman. 2020. On Interactive Machine Learning and the Potential of Cognitive Feedback. arXiv preprint arXiv:2003.10365(2020).
[22]
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, 2019. Pytorch: An imperative style, high-performance deep learning library. In Advances in neural information processing systems. 8026–8037.
[23]
Gonzalo Ramos, Jina Suh, Soroush Ghorashi, Christopher Meek, Richard Banks, Saleema Amershi, Rebecca Fiebrink, Alison Smith-Renner, and Gagan Bansal. 2019. Emerging Perspectives in Human-Centered Machine Learning. In Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems. 1–8.
[24]
Carsten Rother, Vladimir Kolmogorov, and Andrew Blake. 2004. ” GrabCut” interactive foreground extraction using iterated graph cuts. ACM transactions on graphics (TOG) 23, 3 (2004), 309–314.
[25]
Isabella Seeber, Eva Bittner, Robert O Briggs, Triparna de Vreede, Gert-Jan De Vreede, Aaron Elkins, Ronald Maier, Alexander B Merz, Sarah Oeste-Reiß, Nils Randrup, 2020. Machines as teammates: A research agenda on AI in team collaboration. Information & management 57, 2 (2020), 103174.
[26]
Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra. 2017. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE international conference on computer vision. 618–626.
[27]
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, 2016. Mastering the game of Go with deep neural networks and tree search. Nature 529, 7587 (2016), 484–489.
[28]
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. 2013. Deep inside convolutional networks: Visualising image classification models and saliency maps. arXiv preprint arXiv:1312.6034(2013).
[29]
Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, and Martin Riedmiller. 2015. Striving for simplicity: The all convolutional net. In Proceedings of International Conference on Learning Representations (ICLR) Workshop.
[30]
K. P. Subbalakshmi, Aram Galstyan, Rama Chellappa, and Charles Clancy. 2018. Sensemaking Research Roadmap. Technical Report. Defense Technical Information Center.
[31]
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. 2015. Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition. 1–9.
[32]
Meng Tang, Federico Perazzi, Abdelaziz Djelouah, Ismail Ben Ayed, Christopher Schroers, and Yuri Boykov. 2018. On regularized losses for weakly-supervised cnn segmentation. In Proceedings of the European Conference on Computer Vision (ECCV). 507–522.
[33]
Adriana S Vivacqua, Roberto Stelling, Ana Cristina Bicharra Garcia, and Livia C Gouvea. 2019. Explanations and sensemaking with AI and HCI. In Proceedings of the IX Latin American Conference on Human Computer Interaction. 1–4.
[34]
John Wenskovitch and Chris North. 2020. Interactive Artificial Intelligence: Designing for the ”Two Black Boxes” Problem. Computer 53, 8 (2020), 29–39.
[35]
Matthew D Zeiler and Rob Fergus. 2014. Visualizing and understanding convolutional networks. In European conference on computer vision. Springer, 818–833.
[36]
Jianming Zhang, Sarah Adel Bargal, Zhe Lin, Jonathan Brandt, Xiaohui Shen, and Stan Sclaroff. 2018. Top-down neural attention by excitation backprop. International Journal of Computer Vision 126, 10 (2018), 1084–1102.
[37]
Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba. 2016. Learning deep features for discriminative localization. In Proceedings of the IEEE conference on computer vision and pattern recognition. 2921–2929.

Cited By

View all
  • (2024)Visual attention prompted prediction and learningProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/610(5517-5525)Online publication date: 3-Aug-2024
  • (2024)Magic Camera: An AI Drawing Game Supporting Instantaneous Story Creation for ChildrenProceedings of the 23rd Annual ACM Interaction Design and Children Conference10.1145/3628516.3659386(738-743)Online publication date: 17-Jun-2024
  • (2024)Explainable AI for lung nodule detection and classification in CT imagesMedical Imaging 2024: Computer-Aided Diagnosis10.1117/12.3008472(105)Online publication date: 3-Apr-2024
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
CHI EA '21: Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems
May 2021
2965 pages
ISBN:9781450380959
DOI:10.1145/3411763
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 May 2021

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. attention map
  2. explainable AI
  3. image classification
  4. interactive sensemaking
  5. scribble interaction

Qualifiers

  • Poster
  • Research
  • Refereed limited

Conference

CHI '21
Sponsor:

Acceptance Rates

Overall Acceptance Rate 6,164 of 23,696 submissions, 26%

Upcoming Conference

CHI 2025
ACM CHI Conference on Human Factors in Computing Systems
April 26 - May 1, 2025
Yokohama , Japan

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)142
  • Downloads (Last 6 weeks)10
Reflects downloads up to 20 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2024)Visual attention prompted prediction and learningProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/610(5517-5525)Online publication date: 3-Aug-2024
  • (2024)Magic Camera: An AI Drawing Game Supporting Instantaneous Story Creation for ChildrenProceedings of the 23rd Annual ACM Interaction Design and Children Conference10.1145/3628516.3659386(738-743)Online publication date: 17-Jun-2024
  • (2024)Explainable AI for lung nodule detection and classification in CT imagesMedical Imaging 2024: Computer-Aided Diagnosis10.1117/12.3008472(105)Online publication date: 3-Apr-2024
  • (2024)Effective Guidance for Model Attention with Simple Yes-no Annotations2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10825776(1019-1028)Online publication date: 15-Dec-2024
  • (2024)A multidimensional taxonomy for learner-AI interactionEducation and Information Technologies10.1007/s10639-024-12546-w29:14(18361-18378)Online publication date: 8-Mar-2024
  • (2024)Robust explanation supervision for false positive reduction in pulmonary nodule detectionMedical Physics10.1002/mp.1693751:3(1687-1701)Online publication date: 15-Jan-2024
  • (2023)Efficient Human-in-the-loop System for Guiding DNNs AttentionProceedings of the 28th International Conference on Intelligent User Interfaces10.1145/3581641.3584074(294-306)Online publication date: 27-Mar-2023
  • (2023)AI Knowledge: Improving AI Delegation through Human EnablementProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3580794(1-17)Online publication date: 19-Apr-2023
  • (2023)MAGI: Multi-Annotated Explanation-Guided Learning2023 IEEE/CVF International Conference on Computer Vision (ICCV)10.1109/ICCV51070.2023.00189(1977-1987)Online publication date: 1-Oct-2023
  • (2023)Studying How to Efficiently and Effectively Guide Models with Explanations2023 IEEE/CVF International Conference on Computer Vision (ICCV)10.1109/ICCV51070.2023.00184(1922-1933)Online publication date: 1-Oct-2023
  • Show More Cited By

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format.

HTML Format

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media