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Smart "Error"! Exploring Imperfect AI to Support Creative Ideation

Published: 26 April 2024 Publication History

Abstract

Designers widely accept AI as a partner in the design process for its efficient and intelligent decision-making. However, AI is often not perfect, and AI error often makes humans dumbfounded. Literature has pointed out the value of such AI error, while still leaving its inspiration essence and application strategies uncharted from the practice perspective. This work focuses on bridging the practice gap by looking into and exploiting the imaginative "mislabeled" objects of object detection models. To gain insights into the inspiration of AI "error", we collected a dedicated AI "error" dataset from object detection and invited eight designers to share divergent comments on the "mislabeled" objects. Coding was then performed on the comments, which summarizes the inspiration of AI "error" into six atomic dimensions. Subsequently, we took a step further to an exploratory study, a comparative ideation experiment with 20 designers, investigating how to apply these inspiration dimensions to create ideas. Questionnaire and interview results revealed that essential inspiration of AI "error" could positively activate creativity, especially the "Outline" dimension. A design model CETR is then formulated by summarizing the application of atomic inspiration of "error" into four forms of creativity, which could be taken as a guideline for cooperative design with AI "error". In addition, we also sketch two approaches to generate more inspiring and applicable AI "error", elaborate on two principal characteristics of AI "error" for promoting creativity, and propose three strategies for better co-creating with AI "error". Finally, we provide insight into design research about AI self-awareness and human-AI collaboration.

References

[1]
Agathe Balayn, Natasa Rikalo, Christoph Lofi, Jie Yang, and Alessandro Bozzon. 2022. How Can Explainability Methods Be Used to Support Bug Identification in Computer Vision Models?. In Proceeding of CHI Conference on Human Factors in Computing Systems (New Orleans, LA, USA) (CHI '22). ACM, New York, NY, USA, 1--16. https: //doi.org/10.1145/3491102.3517474
[2]
Gagan Bansal, TongshuangWu, Joyce Zhou, Raymond Fok, Besmira Nushi, Ece Kamar, Marco Tulio Ribeiro, and Daniel Weld. 2021. Does the Whole Exceed Its Parts? The Effect of AI Explanations on Complementary Team Performance. In Proceeding of CHI Conference on Human Factors in Computing Systems (Yokohama, Japan) (CHI '21). ACM, New York, NY, USA, 1--16. https://doi.org/10.1145/3411764.3445717
[3]
Jesse Josua Benjamin, Arne Berger, Nick Merrill, and James Pierce. 2021. Machine Learning Uncertainty as a Design Material: A Post-Phenomenological Inquiry. In Proceeding of CHI Conference on Human Factors in Computing Systems (Yokohama, Japan) (CHI '21). ACM, New York, NY, USA, 1--14. https://doi.org/10.1145/3411764.3445481
[4]
Christopher M. Bishop. 2006. Pattern Recognition and Machine Learning (Information Science and Statistics). Springer- Verlag, Berlin, Heidelberg.
[5]
Alexey Bochkovskiy, Chien-Yao Wang, and Hong-Yuan Mark Liao. 2020. YOLOv4: Optimal Speed and Accuracy of Object Detection. https://arxiv.org/abs/2004.10934.
[6]
Margaret A Boden. 1998. Creativity and artificial intelligence. Artificial intelligence 103, 1--2 (1998), 347--356.
[7]
Nathalie Bonnardel and Evelyne Marmèche. 2005. Towards supporting evocation processes in creative design: A cognitive approach. International journal of human-computer studies 63, 4--5 (2005), 422--435.
[8]
Rosi Braidotti. 2019. A Theoretical Framework for the Critical Posthumanities. Theory, Culture & Society 36, 6 (Nov 2019), 31--61. https://doi.org/10.1177/0263276418771486
[9]
Virginia Braun and Victoria Clarke. 2006. Using thematic analysis in psychology. Qualitative research in psychology 3, 2 (2006), 77--101.
[10]
Chris Broeckhoven and Anton du Plessis. 2022. Escaping the labyrinth of bioinspiration: biodiversity as key to successful product innovation. Advanced Functional Materials 32, 18 (2022), 2110235.
[11]
Jenna Burrell. 2016. How the Machine ?Thinks': Understanding Opacity in Machine Learning Algorithms. Big Data & Society 3, 1 (Jun 2016), 1--12. https://doi.org/10.1177/2053951715622512
[12]
Joel Chan, Katherine Fu, Christian Schunn, Jonathan Cagan, KristinWood, and Kenneth Kotovsky. 2011. On the Benefits and Pitfalls of Analogies for Innovative Design: Ideation Performance Based on Analogical Distance, Commonness, and Modality of Examples. Journal of Mechanical Design 133, 8 (Aug 2011), 1--11. https://doi.org/10.1115/1.4004396
[13]
Lydia B Chilton, Ecenaz Jen Ozmen, Sam H Ross, and Vivian Liu. 2021. VisiFit: Structuring Iterative Improvement for Novice Designers. In Proceeding of CHI Conference on Human Factors in Computing Systems (Yokohama, Japan) (CHI '21). ACM, New York, NY, USA, 1--14. https://doi.org/10.1145/3411764.3445089
[14]
Saemi Choi, Shun Matsumura, and Kiyoharu Aizawa. 2019. Assist Users' Interactions in Font Search with Unexpected but Useful Concepts Generated by Multimodal Learning. In Proceeding of the International Conference on Multimedia Retrieval (Ottawa, Canada) (ICMR '19). ACM, New York, NY, USA, 235--243. https://doi.org/10.1145/3323873.3325037
[15]
Clark Christopher, Yatskar Mark, and Zettlemoyer Luke. 2019. Don't Take the Easy Way Out: Ensemble Based Methods for Avoiding Known Dataset Biases. In Proceeding of Conference on Empirical Methods in Natural Language Processing and International Joint Conference on Natural Language Processing (EMNLP-IJCNLP '19). Association for Computational Linguistics, Hong Kong, China, 4069--4082.
[16]
John Joon Young Chung, Shiqing He, and Eytan Adar. 2021. The Intersection of Users, Roles, Interactions, and Technologies in Creativity Support Tools. In Proceeding of Conference on Designing Interactive Systems (Virtual Event, USA) (DIS '21). ACM, New York, NY, USA, 1817--1833. https://doi.org/10.1145/3461778.3462050
[17]
Annie Coggan. 2021. The Book of Errors. A Public Space Books, New York, NY, USA. 1--64 pages.
[18]
Nathan Crilly and Carlos Cardoso. 2017. Where next for Research on Fixation, Inspiration and Creativity in Design? Design Studies 50 (May 2017), 1--38. https://doi.org/10.1016/j.destud.2017.02.001
[19]
Geoff Cumming and Sue Finch. 2005. Inference by Eye: Confidence Intervals and How to Read Pictures of Data. The American psychologist 60 (Feb 2005), 170--80. https://doi.org/10.1037/0003-066X.60.2.170
[20]
Graham Dove, Kim Halskov, Jodi Forlizzi, and John Zimmerman. 2017. UX Design Innovation: Challenges for Working with Machine Learning as a Design Material. In Proceeding of CHI Conference on Human Factors in Computing Systems (Denver, Colorado, USA) (CHI '17). ACM, New York, NY, USA, 278--288. https://doi.org/10.1145/3025453. 3025739
[21]
Claudia Eckert and Martin Stacey. 2000. Sources of inspiration: a language of design. Design studies 21, 5 (2000), 523--538.
[22]
Upol Ehsan, Q. Vera Liao, Michael Muller, Mark O. Riedl, and Justin D. Weisz. 2021. Expanding Explainability: Towards Social Transparency in AI Systems. In Proceeding of Conference on Human Factors in Computing Systems (Yokohama, Japan) (CHI '21). Association for Computing Machinery, New York, NY, USA, Article 82, 19 pages. https: //doi.org/10.1145/3411764.3445188
[23]
Michael W Eysenck and Mark T Keane. 2015. Cognitive psychology: A student'handbook. Psychology press, London, UK.
[24]
Jiaxin Fan, Qi Yan, Mohan Li, Guanqun Qu, and Yang Xiao. 2022. A Survey on Data Poisoning Attacks and Defenses. In 2022 7th IEEE International Conference on Data Science in Cyberspace (DSC) (11--13). IEEE, Guilin, NY, USA, 48--55.
[25]
Ronald A. Finke, Thomas B.Ward, and Steven M. Smith. 1992. Creative Cognition: Theory, Research, and Applications. The MIT Press, Cambridge, MA, USA. 1--256 pages.
[26]
Craig Fox and Gülden Ülkümen. 2011. Distinguishing Two Dimensions of Uncertainty. United Kingdom: Universitetsforlaget, Los Angeles, Chapter 1, 21--35. https://doi.org/10.2139/ssrn.3695311
[27]
Jonas Frich, Lindsay MacDonald Vermeulen, Christian Remy, Michael Mose Biskjaer, and Peter Dalsgaard. 2019. Mapping the Landscape of Creativity Support Tools in HCI. In Proceeding of CHI Conference on Human Factors in Computing Systems (Glasgow, Scotland, Uk) (CHI '19). ACM, New York, NY, USA, 1--18. https://doi.org/10.1145/ 3290605.3300619
[28]
William W Gaver, Andrew Boucher, Sarah Pennington, and Brendan Walker. 2004. Cultural probes and the value of uncertainty. Interactions 11, 5 (2004), 53--56.
[29]
Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard Zemel, Wieland Brendel, Matthias Bethge, and Felix A Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2, 11 (2020), 665--673.
[30]
Dedre Gentner. 1983. Structure-Mapping: A Theoretical Framework for Analogy. Cognitive Science 7, 2 (1983), 155--170. https://doi.org/10.1207/s15516709cog0702_3
[31]
John S Gero. 2000. Computational models of innovative and creative design processes. Technological forecasting and social change 64, 2--3 (2000), 183--196.
[32]
Barney G. Glaser and Anselm L. Strauss. 1967. The Discovery of Grounded Theory. Aldine, Chicago, USA. 284 pages.
[33]
Kosa Goucher-Lambert, Jarrod Moss, and Jonathan Cagan. 2019. A Neuroimaging Investigation of Design Ideation with and without Inspirational Stimuli-Understanding the Meaning of near and Far Stimuli. Design Studies 60 (Jan 2019), 1--38. https://doi.org/10.1016/j.destud.2018.07.001
[34]
Bruna Goveia Da Rocha and Kristina Andersen. 2020. Becoming travelers: Enabling the material drift. In DIS 2020 Companion - Companion Publication of the 2020 ACM Designing Interactive Systems Conference. Association for Computing Machinery, Inc, USA, 215--219. https://doi.org/10.1145/3393914.3395881
[35]
Nouchine Hadjikhani, Kestutis Kveraga, Paulami Naik, and Seppo P Ahlfors. 2009. Early (N170) activation of facespecific cortex by face-like objects. Neuroreport 20, 4 (2009), 403.
[36]
Dan Hendrycks and Thomas G. Dietterich. 2019. Benchmarking Neural Network Robustness to Common Corruptions and Perturbations. In Proceeding of International Conference on Learning Representations. OpenReview.net, New Orleans, USA, 7310--7319.
[37]
Dan Hendrycks, Kevin Zhao, Steven Basart, Jacob Steinhardt, and Dawn Song. 2021. Natural Adversarial Examples. In Proceeding of IEEE Conference on Computer Vision and Pattern Recognition (CVPR '21). IEEE, Nashville, TN, USA, 15257--15266. https://doi.org/10.1109/CVPR46437.2021.01501
[38]
Johan F. Hoorn. 2023. Computer-Vision Classification-Algorithms Are Inherently Creative When Error-Prone. In Proceedings of Conference on Virtual-Reality Continuum and Its Applications in Industry (Guangzhou, China) (VRCAI '22). Association for Computing Machinery, New York, NY, USA, Article 31, 9 pages. https://doi.org/10.1145/ 3574131.3574444
[39]
Tom Hope, Ronen Tamari, Daniel Hershcovich, Hyeonsu B Kang, Joel Chan, Aniket Kittur, and Dafna Shahaf. 2022. Scaling Creative Inspiration with Fine-Grained Functional Aspects of Ideas. In Proceeding of CHI Conference on Human Factors in Computing Systems (New Orleans, LA, USA) (CHI '22). ACM, New York, NY, USA, Article 12, 15 pages. https://doi.org/10.1145/3491102.3517434
[40]
Andrew Hugill. 2012. Pataphysics: a useless guide. Mit Press, Cambridge, Massachusetts.
[41]
H Jabbar and Rafiqul Zaman Khan. 2015. Methods to avoid over-fitting and under-fitting in supervised machine learning (comparative study). Computer Science, Communication and Instrumentation Devices 70 (2015), 163--172.
[42]
David G Jansson and Steven M Smith. 1991. Design fixation. Design studies 12, 1 (1991), 3--11.
[43]
Alfred Jarry and Katharine Noel. 1965. Selected Works of Alfred Jarry. Grove Press, New York, NY, USA.
[44]
Laewoo Kang, Steven Jackson, and Trevor Pinch. 2022. The Electronicists: Techno-aesthetic Encounters for Nonlinear and Art-based Inquiry in HCI. In Proceeding of CHI Conference on Human Factors in Computing Systems (CHI '22). ACM, New Orleans, LA, USA, 1--17. https://doi.org/10.1145/3491102.3517506
[45]
Youwen Kang, Zhida Sun, SitongWang, Zeyu Huang, ZimingWu, and Xiaojuan Ma. 2021. MetaMap: Supporting Visual Metaphor Ideation through Multi-Dimensional Example-Based Exploration. In Proceeding of CHI Conference on Human Factors in Computing Systems (Yokohama, Japan) (CHI '21). ACM, New York, NY, USA, Article 427, 15 pages. https://doi.org/10.1145/3411764.3445325
[46]
Alex Kendall and Yarin Gal. 2017. What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?. In Proceeding of International Conference on Neural Information Processing Systems (Long Beach, California, USA) (NIPS'17). Curran Associates Inc., Red Hook, NY, USA, 5580--5590.
[47]
Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C Berg, Wan-Yen Lo, et al. 2023. Segment anything. https://arxiv.org/abs/2304.02643.
[48]
Janin Koch, Andrés Lucero, Lena Hegemann, and Antti Oulasvirta. 2019. May AI?: Design Ideation with Cooperative Contextual Bandits. In Proceeding of CHI Conference on Human Factors in Computing Systems. ACM, Glasgow, Scotland, Uk, 1--12. https://doi.org/10.1145/3290605.3300863
[49]
Vivian Lai, Samuel Carton, Rajat Bhatnagar, Q. Vera Liao, Yunfeng Zhang, and Chenhao Tan. 2022. Human-AI Collaboration via Conditional Delegation: A Case Study of Content Moderation. In Proceeding of CHI Conference on Human Factors in Computing Systems (New Orleans, LA, USA) (CHI '22). ACM, New York, NY, USA, Article 54, 18 pages. https://doi.org/10.1145/3491102.3501999
[50]
Giacomo Lepri, Andrew McPherson, and John Bowers. 2020. Useless, notWorthless: Absurd Making as Critical Practice. In Proceeding of Conference on Designing Interactive Systems (DIS '20). ACM, Eindhoven, Netherlands, 1887--1899. https://doi.org/10.1145/3357236.3395547
[51]
Q. Vera Liao, Daniel Gruen, and Sarah Miller. 2020. Questioning the AI: Informing Design Practices for Explainable AI User Experiences. In Proceeding of CHI Conference on Human Factors in Computing Systems (Honolulu, HI, USA) (CHI '20). ACM, New York, NY, USA, 1--15. https://doi.org/10.1145/3313831.3376590
[52]
Q. Vera Liao, Daniel Gruen, and Sarah Miller. 2020. Questioning the AI: Informing Design Practices for Explainable AI User Experiences. In Proceeding of CHI Conference on Human Factors in Computing Systems (Honolulu, HI, USA) (CHI '20). ACM, New York, NY, USA, 1--15. https://doi.org/10.1145/3313831.3376590
[53]
Han Liu, Vivian Lai, and Chenhao Tan. 2021. Understanding the Effect of Out-of-Distribution Examples and Interactive Explanations on Human-AI Decision Making. Proc. ACM Hum.-Comput. Interact. 5, CSCW2, Article 408 (Oct 2021), 45 pages. https://doi.org/10.1145/3479552
[54]
Ming-Yu Liu, Xun Huang, Jiahui Yu, Ting-Chun Wang, and Arun Mallya. 2021. Generative Adversarial Networks for Image and Video Synthesis: Algorithms and Applications. Proc. IEEE 109, 5 (May 2021), 839--862. https://doi.org/10. 1109/JPROC.2021.3049196
[55]
A.A. Lucero Vera. 2009. Co-designing interactive spaces for and with designers : supporting mood-board making. Ph.D. Dissertation. Industrial Design. https://doi.org/10.6100/IR641288
[56]
Geke Ludden, Barry Kudrowitz, Rick Schifferstein, and Paul Hekkert. 2012. Surprise & humor in product design. Design sensory metaphors in multiple modalities. Humor-international Journal of Humor Research - HUMOR 25, 3 (01 2012), 285--309.
[57]
Deana Mcdonagh and Ian Storer. 2004. Mood Boards as a Design Catalyst and Resource: Researching an Under- Researched Area. The Design Journal 7, 3 (Nov 2004), 16--31. https://doi.org/10.2752/146069204789338424
[58]
Dan McQuillan. 2018. Data science as machinic neoplatonism. Philosophy & Technology 31, 2 (2018), 253--272.
[59]
Philippa Mothersill and V. Michael Bove. 2019. Beyond Average Tools. On the Use of ?Dumb' Computation and Purposeful Ambiguity to Enhance the Creative Process. The Design Journal 22, sup1 (Apr 2019), 1147--1161. https: //doi.org/10.1080/14606925.2019.1594981
[60]
Aadarsh Padiyath and Brian Magerko. 2021. DesAIner: Exploring the Use of "Bad" Generative Adversarial Networks in the Ideation Process of Fashion Design. In Proceeding of Conference on Creativity and Cognition (Virtual Event, Italy) (C&C '21). ACM, New York, NY, USA, Article 54, 3 pages. https://doi.org/10.1145/3450741.3466636
[61]
Savvas Petridis, Hijung Valentina Shin, and Lydia B Chilton. 2021. SymbolFinder: Brainstorming Diverse Symbols Using Local Semantic Networks. In Proceeding of Annual ACM Symposium on User Interface Software and Technology (Virtual Event, USA) (UIST '21). Association for Computing Machinery, New York, NY, USA, 385--399. https://doi.org/ 10.1145/3472749.3474757
[62]
Andreas Reckwitz. 2018. Die Gesellschaft der Singularitäten. In Kultur-Interdisziplinäre Zugänge. Springer, London, Uk, 45--62.
[63]
M. Jane Riddoch and GlynW. Humphreys. 2001. Object Recognition. In The Handbook of Cognitive Neuropsychology: What Deficits Reveal About the Human Mind, B. Rapp (Ed.). Psychology Press/Taylor & Francis, London, England, UK, 45--74.
[64]
Sameh Said-Metwaly, Wim Van den Noortgate, and Eva Kyndt. 2017. Approaches to measuring creativity: A systematic literature review. Creativity. Theories--Research-Applications 4, 2 (2017), 238--275.
[65]
Téo Sanchez, Baptiste Caramiaux, Pierre Thiel, and Wendy E. Mackay. 2022. Deep Learning Uncertainty in Machine Teaching. In Proceeding of International Conference on Intelligent User Interfaces (Helsinki, Finland) (IUI '22). Association for Computing Machinery, New York, NY, USA, 173--190. https://doi.org/10.1145/3490099.3511117
[66]
Feng Shi, Liuqing Chen, Ji Han, and Peter Childs. 2017. A data-driven text mining and semantic network analysis for design information retrieval. Journal of Mechanical Design 139, 11 (2017), 1--14.
[67]
Yang Shi, Yang Wang, Ye Qi, John Chen, Xiaoyao Xu, and Kwan-Liu Ma. 2017. IdeaWall: Improving Creative Collaboration through Combinatorial Visual Stimuli. In Proceeding of ACM Conference on Computer Supported Cooperative Work and Social Computing (Portland, Oregon, USA) (CSCW '17). ACM, New York, NY, USA, 594--603. https://doi.org/10.1145/2998181.2998208
[68]
Paul J Silvia. 2009. Looking past pleasure: anger, confusion, disgust, pride, surprise, and other unusual aesthetic emotions. Psychology of Aesthetics, Creativity, and the Arts 3, 1 (2009), 48.
[69]
Sharon Spall. 1998. Peer Debriefing in Qualitative Research: Emerging Operational Models. Qualitative Inquiry 4 (1998), 280--292. Issue 2.
[70]
Minhyang (Mia) Suh, Emily Youngblom, Michael Terry, and Carrie J Cai. 2021. AI as Social Glue: Uncovering the Roles of Deep Generative AI during Social Music Composition. In Proceeding of Conference on Human Factors in Computing Systems (Yokohama, Japan) (CHI '21). Association for Computing Machinery, New York, NY, USA, Article 582, 11 pages. https://doi.org/10.1145/3411764.3445219
[71]
van der Burg V, Akdag Salah A, Chandrasegaran S, and Lloyd P. 2022. Ceci n'est Pas Une Chaise: Emerging Practices in Designer-AI Collaboration. In Proceeding of the International Conference of Design Research Society. DRS, Bilbao, Spain, 1--17. https://doi.org/10.21606/drs.2022.653
[72]
Shaun Wallace, Brendan Le, Luis A. Leiva, Aman Haq, Ari Kintisch, Gabrielle Bufrem, Linda Chang, and Jeff Huang. 2020. Sketchy: Drawing Inspiration from the Crowd. Proc. ACM Hum.-Comput. Interact. 4, CSCW2, Article 172 (oct 2020), 27 pages. https://doi.org/10.1145/3415243
[73]
Dakuo Wang, Pattie Maes, Xiangshi Ren, Ben Shneiderman, Yuanchun Shi, and Qianying Wang. 2021. Designing AI to Work WITH or FOR People?. In Proceeding of Extended Abstracts of CHI Conference on Human Factors in Computing Systems (Yokohama, Japan) (CHI EA '21). ACM, New York, NY, USA, Article 154, 5 pages. https://doi.org/10.1145/3411763.3450394
[74]
Hao-Chuan Wang, Dan Cosley, and Susan R. Fussell. 2010. Idea Expander: Supporting Group Brainstorming with Conversationally Triggered Visual Thinking Stimuli. In Proceeding of ACM Conference on Computer Supported Cooperative Work (Savannah, Georgia, USA) (CSCW '10). ACM, New York, NY, USA, 103--106. https://doi.org/10.1145/1718918.1718938
[75]
SuchenWang, Yueqi Duan, Henghui Ding, Yap-Peng Tan, Kim-Hui Yap, and Junsong Yuan. 2022. Learning Transferable Human-Object Interaction Detector with Natural Language Supervision. In n Proceeding of IEEE Conference on Computer Vision and Pattern Recognition (Nashville, TN, USA) (CVPR '22). IEEE, Piscataway, NJ, USA, 929--938. https://doi.org/10.1109/CVPR52688.2022.00101
[76]
Elke U Weber and Eric J Johnson. 2009. Mindful judgment and decision making. Annual review of psychology 60 (2009), 1--53.
[77]
Merryl J Wilkenfeld and Thomas B Ward. 2001. Similarity and emergence in conceptual combination. Journal of Memory and Language 45, 1 (2001), 21--38.
[78]
Xiaoshi Wu, Feng Zhu, Rui Zhao, and Hongsheng Li. 2023. CORA: Adapting CLIP for Open-Vocabulary Detection with Region Prompting and Anchor Pre-Matching. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (Vancouver, Canada). Association for Computing Machinery, New York, NY, USA, 7031--7040.
[79]
Xiaotong (Tone) Xu, Rosaleen Xiong, Boyang Wang, David Min, and Steven P. Dow. 2021. IdeateRelate: An Examples Gallery That Helps Creators Explore Ideas in Relation to Their Own. Proceeding of the ACM on Human-Computer Interaction 5, CSCW2 (Oct 2021), 1--18. https://doi.org/10.1145/3479496
[80]
Hiromu Yakura. 2023. A Generative Framework for Designing Interactions to Overcome the Gaps between Humans and Imperfect AIs Instead of Improving the Accuracy of the AIs. In Proceeding of Extended Abstracts of CHI Conference on Human Factors in Computing Systems (Hamburg, Germany) (CHI EA '23). Association for Computing Machinery, New York, NY, USA, 1--5. https://doi.org/10.1145/3544549.3577036
[81]
Lewei Yao, Jianhua Han, Xiaodan Liang, Dan Xu,Wei Zhang, Zhenguo Li, and Hang Xu. 2023. DetCLIPv2: Scalable Open- Vocabulary Object Detection Pre-training via Word-Region Alignment. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (Vancouver, Canada). Association for Computing Machinery, New York, NY, USA, 23497--23506.
[82]
Lixiu Yu, Aniket Kittur, and Robert E. Kraut. 2016. Encouraging ?Outside- the- Box" Thinking in Crowd Innovation Through Identifying Domains of Expertise. In Proceeding of ACM Conference on Computer-Supported Cooperative Work & Social Computing (San Francisco, California, USA) (CSCW '16). ACM, New York, NY, USA, 1214--1222. https: //doi.org/10.1145/2818048.2820025
[83]
Gyeongwo Yun, Kwangmin Cho, Yunwoo Jeong, and Tek-Jin Nam. 2022. Ideasquares: Utilizing Generative Text as a Source of Design Inspiration. In Proceeding of the International Conference of Design Research Society (DRS '22), D. Lockton, S. Lenzi, P. Hekkert, A. Oak, J. Sádaba, and P. Lloyd (Eds.). DRS, Bilbao, Spain, 1--21. https://doi.org/10. 21606/drs.2022.484
[84]
Rui Zhang, Nathan J. McNeese, Guo Freeman, and Geoff Musick. 2021. ?An Ideal Human": Expectations of AI Teammates in Human-AI Teaming. Proc. ACM Hum.-Comput. Interact. 4, CSCW3, Article 246 (Jan 2021), 25 pages. https://doi.org/10.1145/3432945
[85]
Yunfeng Zhang, Q. Vera Liao, and Rachel K. E. Bellamy. 2020. Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Making. In Proceeding of Conference on Fairness, Accountability, and Transparency (Barcelona, Spain) (FAT* '20). Association for Computing Machinery, New York, NY, USA, 295--305. https://doi.org/10.1145/3351095.3372852
[86]
Yiwu Zhong, Jianwei Yang, Pengchuan Zhang, Chunyuan Li, Noel Codella, Liunian Harold Li, Luowei Zhou, Xiyang Dai, Lu Yuan, Yin Li, and Jianfeng Gao. 2022. RegionCLIP: Region-based Language-Image Pretraining. In Proceeding of IEEE Conference on Computer Vision and Pattern Recognition (New Orleans, LA, USA) (CVPR'22). IEEE, Piscataway, NJ, USA, 16772--16782. https://doi.org/10.1109/CVPR52688.2022.01629

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  • (2024)AI and Future-Making: Design, Biases, and Human-Plant InteractionsProceedings of the 27th International Academic Mindtrek Conference10.1145/3681716.3681738(24-35)Online publication date: 8-Oct-2024

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cover image Proceedings of the ACM on Human-Computer Interaction
Proceedings of the ACM on Human-Computer Interaction  Volume 8, Issue CSCW1
CSCW
April 2024
6294 pages
EISSN:2573-0142
DOI:10.1145/3661497
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Published: 26 April 2024
Published in PACMHCI Volume 8, Issue CSCW1

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  1. co-creative
  2. human-ai collaboration
  3. imperfect ai
  4. object detection

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  • (2024)AI and Future-Making: Design, Biases, and Human-Plant InteractionsProceedings of the 27th International Academic Mindtrek Conference10.1145/3681716.3681738(24-35)Online publication date: 8-Oct-2024

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