research-article

Open access

Designing a Direct Feedback Loop between Humans and Convolutional Neural Networks through Local Explanations

Authors:

Tong Steven Sun,

Shubham Khaladkar,

Sungsoo Ray HongAuthors Info & Claims

Proceedings of the ACM on Human-Computer Interaction, Volume 7, Issue CSCW2

Article No.: 338, Pages 1 - 32

https://doi.org/10.1145/3610187

Published: 04 October 2023 Publication History

Abstract

The local explanation provides heatmaps on images to explain how Convolutional Neural Networks (CNNs) derive their output. Due to its visual straightforwardness, the method has been one of the most popular explainable AI (XAI) methods for diagnosing CNNs. Through our formative study (S1), however, we captured ML engineers' ambivalent perspective about the local explanation as a valuable and indispensable envision in building CNNs versus the process that exhausts them due to the heuristic nature of detecting vulnerability. Moreover, steering the CNNs based on the vulnerability learned from the diagnosis seemed highly challenging. To mitigate the gap, we designed DeepFuse, the first interactive design that realizes the direct feedback loop between a user and CNNs in diagnosing and revising CNN's vulnerability using local explanations. DeepFuse helps CNN engineers to systemically search "unreasonable" local explanations and annotate the new boundaries for those identified as unreasonable in a labor-efficient manner. Next, it steers the model based on the given annotation such that the model doesn't introduce similar mistakes. We conducted a two-day study (S2) with 12 experienced CNN engineers. Using DeepFuse, participants made a more accurate and "reasonable" model than the current state-of-the-art. Also, participants found the way DeepFuse guides case-based reasoning can practically improve their current practice. We provide implications for design that explain how future HCI-driven design can move our practice forward to make XAI-driven insights more actionable.

References

[1]

Ashraf Abdul, Christian von der Weth, Mohan Kankanhalli, and Brian Y Lim. 2020. COGAM: measuring and moderating cognitive load in machine learning model explanations. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. 1--14.

[2]

John G Adair. 1984. The Hawthorne effect: a reconsideration of the methodological artifact. Journal of applied psychology, Vol. 69, 2 (1984), 334.

[3]

Saleema Amershi, Max Chickering, Steven M Drucker, Bongshin Lee, Patrice Simard, and Jina Suh. 2015. Modeltracker: Redesigning performance analysis tools for machine learning. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems. 337--346.

Digital Library

[4]

Agathe Balayn, Christoph Lofi, and Geert-Jan Houben. 2021. Managing bias and unfairness in data for decision support: a survey of machine learning and data engineering approaches to identify and mitigate bias and unfairness within data management and analytics systems. The VLDB Journal, Vol. 30, 5 (2021), 739--768.

Digital Library

[5]

Alsallakh Bilal, Amin Jourabloo, Mao Ye, Xiaoming Liu, and Liu Ren. 2017. Do convolutional neural networks learn class hierarchy? IEEE transactions on visualization and computer graphics, Vol. 24, 1 (2017), 152--162.

[6]

William Blanzeisky, Barry Smyth, and Pádraig Cunningham. 2022. Algorithmic Bias and Fairness in Case-Based Reasoning. In International Conference on Case-Based Reasoning. Springer, 48--62.

[7]

Francesco Bodria, Fosca Giannotti, Riccardo Guidotti, Francesca Naretto, Dino Pedreschi, and Salvatore Rinzivillo. 2021. Benchmarking and survey of explanation methods for black box models. arXiv preprint arXiv:2102.13076 (2021).

[8]

John Brooke. 2013. SUS: a retrospective. Journal of usability studies, Vol. 8, 2 (2013), 29--40.

Digital Library

[9]

John Brooke et al. 1996. SUS-A quick and dirty usability scale. Usability evaluation in industry, Vol. 189, 194 (1996), 4--7.

[10]

Antony Brydon. 2019. Why AI Needs Human Input (And Always Will). https://www.forbes.com/sites/forbestechcouncil/2019/10/30/why-ai-needs-human-input-and-always-will/ Retrieved September 10, 2022 from

[11]

Kelly Caine. 2016. Local standards for sample size at CHI. In Proceedings of the 2016 CHI conference on human factors in computing systems. 981--992.

Digital Library

[12]

Aditya Chattopadhay, Anirban Sarkar, Prantik Howlader, and Vineeth N Balasubramanian. 2018. Grad-cam: Generalized gradient-based visual explanations for deep convolutional networks. In 2018 IEEE winter conference on applications of computer vision (WACV). IEEE, 839--847.

[13]

Justin Cheng and Michael S Bernstein. 2015. Flock: Hybrid crowd-machine learning classifiers. In Proceedings of the 18th ACM conference on computer supported cooperative work & social computing. 600--611.

Digital Library

[14]

Minsuk Choi, Cheonbok Park, Soyoung Yang, Yonggyu Kim, Jaegul Choo, and Sungsoo Ray Hong. 2019. Aila: Attentive interactive labeling assistant for document classification through attention-based deep neural networks. In Proceedings of the 2019 CHI conference on human factors in computing systems. 1--12.

Digital Library

[15]

Alexandra Chouldechova and Aaron Roth. 2018. The frontiers of fairness in machine learning. arXiv preprint arXiv:1810.08810 (2018).

[16]

Chaeyeon Chung, Jung Soo Lee, Kyungmin Park, Junsoo Lee, Jaegul Choo, and Sungsoo Ray Hong. 2021. Understanding Human-side Impact of Sequencing Images in Batch Labeling for Subjective Tasks. Proceedings of the ACM on Human-Computer Interaction CSCW (2021).

[17]

John Joon Young Chung, Jean Y Song, Sindhu Kutty, Sungsoo Ray Hong, Juho Kim, and Walter S Lasecki. 2019. Efficient Elicitation Approaches to Estimate Collective Crowd Answers. Proceedings of the ACM on Human-Computer Interaction, Vol. 3, CSCW (2019), 1--25. https://doi.org/10.1145/3359164

Digital Library

[18]

Dennis Collaris and Jarke J van Wijk. 2020. ExplainExplore: Visual exploration of machine learning explanations. In 2020 IEEE Pacific Visualization Symposium (PacificVis). IEEE, 26--35.

[19]

Jesus M Darias, Marta Caro-Mart'inez, Belén D'iaz-Agudo, and Juan A Recio-Garcia. 2022. Using Case-Based Reasoning for Capturing Expert Knowledge on Explanation Methods. In International Conference on Case-Based Reasoning. Springer, 3--17.

[20]

Jay DeYoung, Sarthak Jain, Nazneen Fatema Rajani, Eric Lehman, Caiming Xiong, Richard Socher, and Byron C Wallace. 2019. ERASER: A benchmark to evaluate rationalized NLP models. arXiv preprint arXiv:1911.03429 (2019).

[21]

Gordon Diaper. 1990. The Hawthorne effect: A fresh examination. Educational studies, Vol. 16, 3 (1990), 261--267.

[22]

John J Dudley and Per Ola Kristensson. 2018. A review of user interface design for interactive machine learning. ACM Transactions on Interactive Intelligent Systems (TiiS), Vol. 8, 2 (2018), 1--37.

Digital Library

[23]

Shannon Leigh Eggers and Char Sample. 2020. Vulnerabilities in Artificial Intelligence and Machine Learning Applications and Data. Technical Report. Idaho National Lab.(INL), Idaho Falls, ID (United States).

[24]

Jerry Alan Fails and Dan R Olsen Jr. 2003. Interactive machine learning. In Proceedings of the 8th international conference on Intelligent user interfaces. 39--45.

Digital Library

[25]

Hiroshi Fukui, Tsubasa Hirakawa, Takayoshi Yamashita, and Hironobu Fujiyoshi. 2019. Attention branch network: Learning of attention mechanism for visual explanation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 10705--10714.

[26]

Yuyang Gao, Siyi Gu, Junji Jiang, Sungsoo Ray Hong, Dazhou Yu, and Liang Zhao. 2022a. Going Beyond XAI: A Systematic Survey for Explanation-Guided Learning. arXiv preprint arXiv:2212.03954 (2022).

[27]

Yuyang Gao, Tong Sun, Rishab Bhatt, Dazhou Yu, Sungsoo Hong, and Liang Zhao. 2021. GNES: Learning to explain graph neural networks. In 2021 IEEE International Conference on Data Mining (ICDM). IEEE, 131--140.

[28]

Yuyang Gao, Tong Steven Sun, Guangji Bai, Siyi Gu, Sungsoo Ray Hong, and Zhao Liang. 2022b. RES: A Robust Framework for Guiding Visual Explanation. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 432--442.

Digital Library

[29]

Yuyang Gao, Tong Steven Sun, Liang Zhao, and Sungsoo Ray Hong. 2022c. Aligning eyes between humans and deep neural network through interactive attention alignment. Proceedings of the ACM on Human-Computer Interaction, Vol. 6, CSCW2 (2022), 1--28.

Digital Library

[30]

Yolanda Gil, James Honaker, Shikhar Gupta, Yibo Ma, Vito D'Orazio, Daniel Garijo, Shruti Gadewar, Qifan Yang, and Neda Jahanshad. 2019. Towards human-guided machine learning. In Proceedings of the 24th International Conference on Intelligent User Interfaces. 614--624.

Digital Library

[31]

Yolanda Gil and Bart Selman. 2019. A 20-Year Community Roadmap for Artificial Intelligence Research in the US. arXiv preprint arXiv:1908.02624 (2019).

[32]

Max Glockner, Ivan Habernal, and Iryna Gurevych. 2020. Why do you think that? exploring faithful sentence-level rationales without supervision. arXiv preprint arXiv:2010.03384 (2020).

[33]

Nitesh Goyal, Sungsoo Ray Hong, Regan L Mandryk, Toby Jia-Jun Li, Kurt Luther, and Dakuo Wang. 2023. SHAI 2023: Workshop on Designing for Safety in Human-AI Interactions. In Companion Proceedings of the 28th International Conference on Intelligent User Interfaces. 199--201.

[34]

Ben Green and Yiling Chen. 2019. The principles and limits of algorithm-in-the-loop decision making. Proceedings of the ACM on Human-Computer Interaction, Vol. 3, CSCW (2019), 1--24.

Digital Library

[35]

Mark Hall, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, and Ian H Witten. 2009. The WEKA data mining software: an update. ACM SIGKDD explorations newsletter, Vol. 11, 1 (2009), 10--18.

Digital Library

[36]

Foad Hamidi, Morgan Klaus Scheuerman, and Stacy M Branham. 2018. Gender recognition or gender reductionism? The social implications of embedded gender recognition systems. In Proceedings of the 2018 chi conference on human factors in computing systems. 1--13.

Digital Library

[37]

Gillian R Hayes. 2014. Knowing by doing: action research as an approach to HCI. In Ways of Knowing in HCI. Springer, 49--68.

[38]

Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick. 2017. Mask r-cnn. In Proceedings of the IEEE international conference on computer vision. 2961--2969.

[39]

Lisa Anne Hendricks, Kaylee Burns, Kate Saenko, Trevor Darrell, and Anna Rohrbach. 2018. Women also snowboard: Overcoming bias in captioning models. In Proceedings of the European Conference on Computer Vision (ECCV). 771--787.

Digital Library

[40]

Yusuke Hirota, Yuta Nakashima, and Noa Garcia. 2022. Gender and Racial Bias in Visual Question Answering Datasets. arXiv preprint arXiv:2205.08148 (2022).

[41]

Fred Hohman, Kanit Wongsuphasawat, Mary Beth Kery, and Kayur Patel. 2020. Understanding and visualizing data iteration in machine learning. In Proceedings of the 2020 CHI conference on human factors in computing systems. 1--13.

Digital Library

[42]

Sungsoo Ray Hong, Sonia Castelo, Vito D'Orazio, Christopher Benthune, Aecio Santos, Scott Langevin, David Jonker, Enrico Bertini, and Juliana Freire. 2020a. Towards Evaluating Exploratory Model Building Process with AutoML Systems. arXiv preprint arXiv:2009.00449 (2020).

[43]

Sungsoo Ray Hong, Jessica Hullman, and Enrico Bertini. 2020b. Human Factors in Model Interpretability: Industry Practices, Challenges, and Needs. Proceedings of the ACM on Human-Computer Interaction, Vol. 4 (2020), 1--26.

Digital Library

[44]

Sungsoo Ray Hong, Jorge Piazentin Ono, Juliana Freire, and Enrico Bertini. 2019. Disseminating Machine Learning to domain experts: Understanding challenges and opportunities in supporting a model building process. In CHI 2019 Workshop, Emerging Perspectives in Human-Centered Machine Learning. ACM.

[45]

Scott E Hudson and Jennifer Mankoff. 2014. Concepts, values, and methods for technical human-computer interaction research. In Ways of Knowing in HCI. Springer, 69--93.

[46]

Aya Abdelsalam Ismail, Hector Corrada Bravo, and Soheil Feizi. 2021. Improving deep learning interpretability by saliency guided training. Advances in Neural Information Processing Systems, Vol. 34 (2021), 26726--26739.

[47]

Minsuk Kahng, Pierre Y Andrews, Aditya Kalro, and Duen Horng Chau. 2017. ActiVis: Visual exploration of industry-scale deep neural network models. IEEE transactions on visualization and computer graphics, Vol. 24, 1 (2017), 88--97.

[48]

Minsuk Kahng, Dezhi Fang, and Duen Horng Chau. 2016. Visual exploration of machine learning results using data cube analysis. In Proceedings of the Workshop on Human-In-the-Loop Data Analytics. 1--6.

Digital Library

[49]

Tharindu Kaluarachchi, Andrew Reis, and Suranga Nanayakkara. 2021. A review of recent deep learning approaches in human-centered machine learning. Sensors, Vol. 21, 7 (2021), 2514.

[50]

Beomyoung Kim, Sangeun Han, and Junmo Kim. 2021. Discriminative region suppression for weakly-supervised semantic segmentation. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35. 1754--1761.

[51]

James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, et al. 2017. Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences, Vol. 114, 13 (2017), 3521--3526.

[52]

Michael Koch, Kai von Luck, Jan Schwarzer, and Susanne Draheim. 2018. The novelty effect in large display deployments--Experiences and lessons-learned for evaluating prototypes. In Proceedings of 16th European conference on computer-supported cooperative work-exploratory papers. European Society for Socially Embedded Technologies (EUSSET).

[53]

Josua Krause, Aritra Dasgupta, Jordan Swartz, Yindalon Aphinyanaphongs, and Enrico Bertini. 2017. A workflow for visual diagnostics of binary classifiers using instance-level explanations. In 2017 IEEE Conference on Visual Analytics Science and Technology (VAST). IEEE, 162--172.

[54]

Josua Krause, Adam Perer, and Kenney Ng. 2016. Interacting with predictions: Visual inspection of black-box machine learning models. In Proceedings of the 2016 CHI conference on human factors in computing systems. 5686--5697.

Digital Library

[55]

Derek Layder. 1998. Sociological practice: Linking theory and social research. Sage.

[56]

Jungsoo Lee, Eungyeup Kim, Juyoung Lee, Jihyeon Lee, and Jaegul Choo. 2021a. Learning debiased representation via disentangled feature augmentation. Advances in Neural Information Processing Systems, Vol. 34 (2021), 25123--25133.

[57]

Seungho Lee, Minhyun Lee, Jongwuk Lee, and Hyunjung Shim. 2021b. Railroad is not a train: Saliency as pseudo-pixel supervision for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 5495--5505.

[58]

Zewen Li, Fan Liu, Wenjie Yang, Shouheng Peng, and Jun Zhou. 2021. A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021).

[59]

Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick. 2014. Microsoft coco: Common objects in context. In European conference on computer vision. Springer, 740--755.

[60]

Chang Liu, Yu Cao, Marlon Alcantara, Benyuan Liu, Maria Brunette, Jesus Peinado, and Walter Curioso. 2017. TX-CNN: Detecting tuberculosis in chest X-ray images using convolutional neural network. In 2017 IEEE international conference on image processing (ICIP). IEEE, 2314--2318.

Digital Library

[61]

Jiakun Liu, Qiao Huang, Xin Xia, Emad Shihab, David Lo, and Shanping Li. 2020. Is using deep learning frameworks free? characterizing technical debt in deep learning frameworks. In Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering: Software Engineering in Society. 1--10.

Digital Library

[62]

Scott M Lundberg, Gabriel Erion, Hugh Chen, Alex DeGrave, Jordan M Prutkin, Bala Nair, Ronit Katz, Jonathan Himmelfarb, Nisha Bansal, and Su-In Lee. 2020. From local explanations to global understanding with explainable AI for trees. Nature machine intelligence, Vol. 2, 1 (2020), 56--67.

[63]

Yao Ming. 2017. A survey on visualization for explainable classifiers. (2017).

[64]

Yao Ming, Shaozu Cao, Ruixiang Zhang, Zhen Li, Yuanzhe Chen, Yangqiu Song, and Huamin Qu. 2017. Understanding hidden memories of recurrent neural networks. In 2017 IEEE Conference on Visual Analytics Science and Technology (VAST). IEEE, 13--24.

[65]

Yao Ming, Huamin Qu, and Enrico Bertini. 2018. Rulematrix: Visualizing and understanding classifiers with rules. IEEE transactions on visualization and computer graphics, Vol. 25, 1 (2018), 342--352.

[66]

Masahiro Mitsuhara, Hiroshi Fukui, Yusuke Sakashita, Takanori Ogata, Tsubasa Hirakawa, Takayoshi Yamashita, and Hironobu Fujiyoshi. 2019. Embedding human knowledge into deep neural network via attention map. arXiv preprint arXiv:1905.03540 (2019).

[67]

Sina Mohseni, Niloofar Zarei, and Eric D Ragan. 2021. A multidisciplinary survey and framework for design and evaluation of explainable AI systems. ACM Transactions on Interactive Intelligent Systems (TiiS), Vol. 11, 3--4 (2021), 1--45.

Digital Library

[68]

Mohammed Bany Muhammad and Mohammed Yeasin. 2020. Eigen-cam: Class activation map using principal components. In 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 1--7.

[69]

Don Norman. 2013. The design of everyday things: Revised and expanded edition. Basic books.

[70]

German I Parisi, Ronald Kemker, Jose L Part, Christopher Kanan, and Stefan Wermter. 2019. Continual lifelong learning with neural networks: A review. Neural Networks, Vol. 113 (2019), 54--71.

Digital Library

[71]

Haekyu Park, Nilaksh Das, Rahul Duggal, Austin P Wright, Omar Shaikh, Fred Hohman, and Duen Horng Polo Chau. 2021. Neurocartography: Scalable automatic visual summarization of concepts in deep neural networks. IEEE Transactions on Visualization and Computer Graphics, Vol. 28, 1 (2021), 813--823.

Digital Library

[72]

Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems, Vol. 32 (2019).

[73]

Nicola Pezzotti. 2019. Dimensionality-Reduction Algorithms for Progressive Visual Analytics. (2019).

[74]

Donghao Ren, Saleema Amershi, Bongshin Lee, Jina Suh, and Jason D Williams. 2016. Squares: Supporting interactive performance analysis for multiclass classifiers. IEEE transactions on visualization and computer graphics, Vol. 23, 1 (2016), 61--70.

Digital Library

[75]

Shiori Sagawa, Aditi Raghunathan, Pang Wei Koh, and Percy Liang. 2020. An investigation of why overparameterization exacerbates spurious correlations. In International Conference on Machine Learning. PMLR, 8346--8356.

[76]

Johnny Salda na. 2015. The coding manual for qualitative researchers. Sage.

[77]

Aécio Santos, Sonia Castelo, Cristian Felix, Jorge Piazentin Ono, Bowen Yu, Sungsoo Ray Hong, Cláudio T Silva, Enrico Bertini, and Juliana Freire. 2019. Visus: An interactive system for automatic machine learning model building and curation. In Proceedings of the Workshop on Human-In-the-Loop Data Analytics. 1--7.

Digital Library

[78]

Irving Seidman. 2006. Interviewing as qualitative research: A guide for researchers in education and the social sciences. Teachers college press.

[79]

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.

[80]

Xiaoting Shao, Tjitze Rienstra, Matthias Thimm, and Kristian Kersting. 2020. Towards understanding and arguing with classifiers: Recent progress. Datenbank-Spektrum, Vol. 20, 2 (2020), 171--180.

[81]

Gary B Shelly, Thomas J Cashman, and Joy L Starks. 2008. Adobe Photoshop CS3: Complete concepts and techniques. Course Technology Press.

[82]

Herbert A Simon. 1981. The sciences of the artificial, 1969. Massachusetts Institute of Technology (1981).

Digital Library

[83]

Krishna Kumar Singh, Dhruv Mahajan, Kristen Grauman, Yong Jae Lee, Matt Feiszli, and Deepti Ghadiyaram. 2020. Don't judge an object by its context: learning to overcome contextual bias. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 11070--11078.

[84]

Xuelin Situ, Ingrid Zukerman, Cecile Paris, Sameen Maruf, and Gholamreza Haffari. 2021. Learning to explain: Generating stable explanations fast. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 5340--5355.

[85]

Timo Speith. 2022. A Review of Taxonomies of Explainable Artificial Intelligence (XAI) Methods. In 2022 ACM Conference on Fairness, Accountability, and Transparency. 2239--2250.

Digital Library

[86]

Thilo Spinner, Udo Schlegel, Hanna Sch"afer, and Mennatallah El-Assady. 2019. explAIner: A visual analytics framework for interactive and explainable machine learning. IEEE transactions on visualization and computer graphics, Vol. 26, 1 (2019), 1064--1074.

[87]

Jiawei Su, Danilo Vasconcellos Vargas, and Kouichi Sakurai. 2019. Attacking convolutional neural network using differential evolution. IPSJ Transactions on Computer Vision and Applications, Vol. 11, 1 (2019), 1--16.

[88]

Justin Talbot, Bongshin Lee, Ashish Kapoor, and Desney S Tan. 2009. EnsembleMatrix: interactive visualization to support machine learning with multiple classifiers. In Proceedings of the SIGCHI conference on human factors in computing systems. 1283--1292.

Digital Library

[89]

Dakuo Wang, Justin D Weisz, Michael Muller, Parikshit Ram, Werner Geyer, Casey Dugan, Yla Tausczik, Horst Samulowitz, and Alexander Gray. 2019a. Human-AI collaboration in data science: Exploring data scientists' perceptions of automated AI. Proceedings of the ACM on Human-Computer Interaction, Vol. 3, CSCW (2019), 1--24.

Digital Library

[90]

Keze Wang, Dongyu Zhang, Ya Li, Ruimao Zhang, and Liang Lin. 2016. Cost-effective active learning for deep image classification. IEEE Transactions on Circuits and Systems for Video Technology, Vol. 27, 12 (2016), 2591--2600.

Digital Library

[91]

Tianlu Wang, Jieyu Zhao, Mark Yatskar, Kai-Wei Chang, and Vicente Ordonez. 2019b. Balanced datasets are not enough: Estimating and mitigating gender bias in deep image representations. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 5310--5319.

[92]

Zijie J Wang, Robert Turko, and Duen Horng Chau. 2021. Dodrio: Exploring transformer models with interactive visualization. arXiv preprint arXiv:2103.14625 (2021).

[93]

Zijie J Wang, Robert Turko, Omar Shaikh, Haekyu Park, Nilaksh Das, Fred Hohman, Minsuk Kahng, and Duen Horng Chau. 2020. CNN 101: Interactive visual learning for convolutional neural networks. In Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems. 1--7.

Digital Library

[94]

Greta Warren, Barry Smyth, and Mark T Keane. 2022. ?Better" Counterfactuals, Ones People Can Understand: Psychologically-Plausible Case-Based Counterfactuals Using Categorical Features for Explainable AI (XAI). In International Conference on Case-Based Reasoning. Springer, 63--78.

Digital Library

[95]

James Wexler, Mahima Pushkarna, Tolga Bolukbasi, Martin Wattenberg, Fernanda Viégas, and Jimbo Wilson. 2019. The what-if tool: Interactive probing of machine learning models. IEEE transactions on visualization and computer graphics, Vol. 26, 1 (2019), 56--65.

[96]

Kanit Wongsuphasawat, Daniel Smilkov, James Wexler, Jimbo Wilson, Dandelion Mane, Doug Fritz, Dilip Krishnan, Fernanda B Viégas, and Martin Wattenberg. 2017. Visualizing dataflow graphs of deep learning models in tensorflow. IEEE transactions on visualization and computer graphics, Vol. 24, 1 (2017), 1--12.

[97]

Chuan Yan, John Joon Young Chung, Yoon Kiheon, Yotam Gingold, Eytan Adar, and Sungsoo Ray Hong. 2022. FlatMagic: Improving Flat Colorization through AI-Driven Design for Digital Comic Professionals. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems. 1--17.

Digital Library

[98]

Huanrui Yang, Jingyang Zhang, Hongliang Dong, Nathan Inkawhich, Andrew Gardner, Andrew Touchet, Wesley Wilkes, Heath Berry, and Hai Li. 2020. DVERGE: diversifying vulnerabilities for enhanced robust generation of ensembles. Advances in Neural Information Processing Systems, Vol. 33 (2020), 5505--5515.

[99]

Qian Yang, Alex Scuito, John Zimmerman, Jodi Forlizzi, and Aaron Steinfeld. 2018a. Investigating how experienced UX designers effectively work with machine learning. In Proceedings of the 2018 designing interactive systems conference. 585--596.

Digital Library

[100]

Qian Yang, Jina Suh, Nan-Chen Chen, and Gonzalo Ramos. 2018b. Grounding interactive machine learning tool design in how non-experts actually build models. In Proceedings of the 2018 designing interactive systems conference. 573--584.

Digital Library

[101]

Omar Zaidan, Jason Eisner, and Christine Piatko. 2007. Using ?annotator rationales" to improve machine learning for text categorization. In Human language technologies 2007: The conference of the North American chapter of the association for computational linguistics; proceedings of the main conference. 260--267.

[102]

Amy X Zhang, Michael Muller, and Dakuo Wang. 2020. How do data science workers collaborate? roles, workflows, and tools. Proceedings of the ACM on Human-Computer Interaction, Vol. 4, CSCW1 (2020), 1--23.

Digital Library

[103]

Zijian Zhang, Jaspreet Singh, Ujwal Gadiraju, and Avishek Anand. 2019. Dissonance between human and machine understanding. Proceedings of the ACM on Human-Computer Interaction, Vol. 3, CSCW (2019), 1--23.

Digital Library

[104]

Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang. 2017. Men also like shopping: Reducing gender bias amplification using corpus-level constraints. arXiv preprint arXiv:1707.09457 (2017).

Cited By

Han YGilbert TTan XWard JKostakos VKay JHoang T(2024)The Wand Chooses the IMU - Open Source Hardware for Synchronising Wearables using MagnetometersCompanion of the 2024 on ACM International Joint Conference on Pervasive and Ubiquitous Computing10.1145/3675094.3678485(939-943)Online publication date: 5-Oct-2024
https://dl.acm.org/doi/10.1145/3675094.3678485
Ganguly AYan CChung JSun TKiheon YGingold YHong S(2024)ShadowMagic: Designing Human-AI Collaborative Support for Comic Professionals’ ShadowingProceedings of the 37th Annual ACM Symposium on User Interface Software and Technology10.1145/3654777.3676332(1-15)Online publication date: 13-Oct-2024
https://dl.acm.org/doi/10.1145/3654777.3676332
Gao YGu SJiang JHong SYu DZhao L(2024)Going Beyond XAI: A Systematic Survey for Explanation-Guided LearningACM Computing Surveys10.1145/364407356:7(1-39)Online publication date: 9-Apr-2024
https://dl.acm.org/doi/10.1145/3644073
Show More Cited By

Index Terms

Designing a Direct Feedback Loop between Humans and Convolutional Neural Networks through Local Explanations
1. Computing methodologies
  1. Machine learning
    1. Learning settings
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction paradigms

Recommendations

Aligning Eyes between Humans and Deep Neural Network through Interactive Attention Alignment
CSCW

While Deep Neural Networks (DNNs) are deriving the major innovations through their powerful automation, we are also witnessing the peril behind automation as a form of bias, such as automated racism, gender bias, and adversarial bias. As the societal ...
Human Action Recognition using Pre-trained Convolutional Neural Networks
VSIP '20: Proceedings of the 2020 2nd International Conference on Video, Signal and Image Processing

Recognition of human action is one of the challenges in the field of artificial intelligence. Deep learning model has become a research issue in action recognition applications due to its ability to outperform traditional machine learning approaches. ...
Fake Faces Identification via Convolutional Neural Network
IH&MMSec '18: Proceedings of the 6th ACM Workshop on Information Hiding and Multimedia Security

Generative Adversarial Network (GAN) is a prominent generative model that are widely used in various applications. Recent studies have indicated that it is possible to obtain fake face images with a high visual quality based on this novel model. If ...

Comments

Information & Contributors

Information

Published In

cover image Proceedings of the ACM on Human-Computer Interaction

Proceedings of the ACM on Human-Computer Interaction Volume 7, Issue CSCW2

CSCW

October 2023

4055 pages

EISSN:2573-0142

DOI:10.1145/3626953

Editor:
Jeff Nichols
Apple Inc., United States

Issue’s Table of Contents

Copyright © 2023 ACM.

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 the author(s) 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].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 October 2023

Published in PACMHCI Volume 7, Issue CSCW2

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

NSF (National Science Foundation)

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

9
Total Citations
View Citations
511
Total Downloads

Downloads (Last 12 months)457
Downloads (Last 6 weeks)54

Reflects downloads up to 09 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Han YGilbert TTan XWard JKostakos VKay JHoang T(2024)The Wand Chooses the IMU - Open Source Hardware for Synchronising Wearables using MagnetometersCompanion of the 2024 on ACM International Joint Conference on Pervasive and Ubiquitous Computing10.1145/3675094.3678485(939-943)Online publication date: 5-Oct-2024
https://dl.acm.org/doi/10.1145/3675094.3678485
Ganguly AYan CChung JSun TKiheon YGingold YHong S(2024)ShadowMagic: Designing Human-AI Collaborative Support for Comic Professionals’ ShadowingProceedings of the 37th Annual ACM Symposium on User Interface Software and Technology10.1145/3654777.3676332(1-15)Online publication date: 13-Oct-2024
https://dl.acm.org/doi/10.1145/3654777.3676332
Gao YGu SJiang JHong SYu DZhao L(2024)Going Beyond XAI: A Systematic Survey for Explanation-Guided LearningACM Computing Surveys10.1145/364407356:7(1-39)Online publication date: 9-Apr-2024
https://dl.acm.org/doi/10.1145/3644073
Ara ZSalemi HHong SSenarath YPeterson SHughes APurohit H(2024)Closing the Knowledge Gap in Designing Data Annotation Interfaces for AI-powered Disaster Management Analytic SystemsProceedings of the 29th International Conference on Intelligent User Interfaces10.1145/3640543.3645214(405-418)Online publication date: 18-Mar-2024
https://dl.acm.org/doi/10.1145/3640543.3645214
Kwon NSun TGao YZhao LWang XKim JHong S(2024)3DPFIX: Improving Remote Novices' 3D Printing Troubleshooting through Human-AI Collaboration DesignProceedings of the ACM on Human-Computer Interaction10.1145/36372888:CSCW1(1-33)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3637288
Stamm JRichardson DWard J(2024)Squeeze and Slide: Real-time continuous self-reports with physiological arousal to evaluate emotional engagement in short films of contemporary danceExtended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613905.3651886(1-7)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613905.3651886
Shao HMartinez-Maldonado REcheverria VYan LGasevic D(2024)Data Storytelling in Data Visualisation: Does it Enhance the Efficiency and Effectiveness of Information Retrieval and Insights Comprehension?Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3643022(1-21)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3643022
Müller R(2024)How Explainable AI Affects Human Performance: A Systematic Review of the Behavioural Consequences of Saliency MapsInternational Journal of Human–Computer Interaction10.1080/10447318.2024.2381929(1-32)Online publication date: 29-Jul-2024
https://doi.org/10.1080/10447318.2024.2381929
Marcus-Quinn AFotiadis TYeratziotis APapadopoulos G(2024)Accessibility of e-books for Secondary School Students in Ireland and CyprusTransforming Media Accessibility in Europe10.1007/978-3-031-60049-4_13(229-245)Online publication date: 20-Aug-2024
https://doi.org/10.1007/978-3-031-60049-4_13

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

Media

Figures

Other

Tables

View Issue’s Table of Contents