research-article

Prototyping Machine Learning Through Diffractive Art Practice

Authors:

Baptiste Caramiaux,

Frederic BevilacquaAuthors Info & Claims

DIS '21: Proceedings of the 2021 ACM Designing Interactive Systems Conference

Pages 2013 - 2025

https://doi.org/10.1145/3461778.3462163

Published: 28 June 2021 Publication History

Abstract

In this paper, we outline a diffractive practice of machine learning (ML) in the frame of material-centered interaction design. To this aim, we review related work in ML, HCI, design, new interfaces for musical expression, and computational art, and introduce two practice-based studies of music performance and robotic art based on interactive machine learning tools, with the hope of revealing the computational materiality of ML, and the potential of embodiment to craft prototypes of ML that reconfigure conceptual or technical approaches to ML. We derive five interference conditions for such art-based ML prototypes—situational whole, small data, shallow model, learnable algorithm, and somaesthetic behaviour—and describe their widening of design and engineering practices of ML prototyping. Finally, we sketch how a process of intra-active machine learning could complement that of interactive machine learning to take materiality as an entry point for ML design within HCI.

References

[1]

Memo Akten, Rebecca Fiebrink, and Mick Grierson. 2019. Learning to see: you are what you see. In ACM SIGGRAPH 2019 Art Gallery. ACM, 1–6.

Digital Library

[2]

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.

Digital Library

[3]

Sofian Audry. 2019. Behavior Morphologies of Machine Learning Agents in Media Artworks. Leonardo (2019), 1–10.

[4]

Sofian Audry. 2020. La matérialité révélatrice de l’apprentissage automatique. Espace: art actuel “IA - Art sans artistes”, 124 (2020), 44–49.

[5]

Sofian Audry, Rosalie Dumont-Gagné, and Hugo Scurto. 2020. Behaviour Aesthetics of Reinforcement Learning in a Robotic Art Installation. In 4th Workshop Workshop on Machine Learning for Creativity and Design at NeurIPS 2020.

[6]

Karen Barad. 2007. Meeting the universe halfway: Quantum physics and the entanglement of matter and meaning. duke university Press.

[7]

Joffrey Becker, Virginie André, and Alain Dutech. 2019. Qualcom: une expérience sur la qualification des comportements d’une lampe robotique. Techniques & Culture. Revue semestrielle d’anthropologie des techniques (2019).

[8]

Steve Benford, Chris Greenhalgh, Andy Crabtree, Martin Flintham, Brendan Walker, Joe Marshall, Boriana Koleva, Stefan Rennick Egglestone, Gabriella Giannachi, Matt Adams, 2013. Performance-led research in the wild. ACM Transactions on Computer-Human Interaction (TOCHI) 20, 3(2013), 1–22.

Digital Library

[9]

Jesse Josua Benjamin, Arne Berger, Nick Merrill, and James Pierce. 2021. Machine Learning Uncertainty as a Design Material: A Post-Phenomenological Inquiry. arXiv preprint arXiv:2101.04035(2021).

[10]

Frederic Bevilacqua, Fabrice Guédy, Norbert Schnell, Emmanuel Fléty, and Nicolas Leroy. 2007. Wireless sensor interface and gesture-follower for music pedagogy. In Proceedings of the 7th International Conference on New Interfaces for Musical Expression (NIME). ACM, 124–129.

Digital Library

[11]

Frédéric Bevilacqua, Rémy Müller, and Norbert Schnell. 2005. MnM: a Max/MSP mapping toolbox. In Proceedings of the 2005 conference on New interfaces for musical expression. National University of Singapore, 85–88.

[12]

Frédéric Bevilacqua, Bruno Zamborlin, Anthony Sypniewski, Norbert Schnell, Fabrice Guédy, and Nicolas Rasamimanana. 2009. Continuous realtime gesture following and recognition. In International gesture workshop. Springer, 73–84.

[13]

Samuel Bianchini, Rémy Bourganel, Emanuele Quinz, Florent Levillain, and Elisabetta Zibetti. 2015. (Mis) behavioral Objects. In Empowering Users through Design. Springer, 129–152.

[14]

Samuel Bianchini, Hiroshi Ishii, Bourganel Rémy, Mahé Emmanuel, Labrune Jean-Baptiste, and Quinz Emanuele. 2014. The misbehavior of animated object. In Proceedings of the 8th International Conference on Tangible, Embedded and Embodied Interaction. 381–384.

[15]

Samuel Bianchini and Emanuele Quinz. 2016. Behavioural Objects I: A Case Study. Vol. 1. Sternberg Press.

[16]

Christopher M Bishop. 2006. Pattern recognition and machine learning. springer.

[17]

Jacob T Browne. 2019. Wizard of Oz Prototyping for Machine Learning Experiences. In Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems. 1–6.

[18]

Lucian Buşoniu, Robert Babuška, and Bart De Schutter. 2010. Multi-agent reinforcement learning: An overview. Innovations in multi-agent systems and applications-1 (2010), 183–221.

[19]

Erik Cambria. 2016. Affective computing and sentiment analysis. IEEE intelligent systems 31, 2 (2016), 102–107.

[20]

Baptiste Caramiaux and Marco Donnarumma. 2020. Artificial Intelligence in Music and Performance: A Subjective Art-Research Inquiry. arXiv preprint arXiv:2007.15843(2020).

[21]

Baptiste Caramiaux, Nicola Montecchio, Atau Tanaka, and Frédéric Bevilacqua. 2015. Adaptive gesture recognition with variation estimation for interactive systems. ACM Transactions on Interactive Intelligent Systems (TiiS) 4, 4(2015), 18.

[22]

Nicholas Davis, Chih-PIn Hsiao, Kunwar Yashraj Singh, Lisa Li, and Brian Magerko. 2016. Empirically studying participatory sense-making in abstract drawing with a co-creative cognitive agent. In Proceedings of the 21st International Conference on Intelligent User Interfaces. ACM, 196–207.

Digital Library

[23]

Nicholas M Davis, Yanna Popova, Ivan Sysoev, Chih-Pin Hsiao, Dingtian Zhang, and Brian Magerko. 2014. Building Artistic Computer Colleagues with an Enactive Model of Creativity. In ICCC. 38–45.

[24]

Kristin N Dew and Daniela K Rosner. 2018. Lessons from the woodshop: Cultivating design with living materials. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. 1–12.

Digital Library

[25]

Stephanie Dinkins. 2020. Community, Art and the Vernacular in Technological Ecosystems. In Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction. 221–221.

Digital Library

[26]

Graham Dove and Anne-Laure Fayard. 2020. Monsters, metaphors, and machine learning. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. 1–17.

Digital Library

[27]

Graham Dove, Kim Halskov, Jodi Forlizzi, and John Zimmerman. 2017. UX Design Innovation: Challenges for Working with Machine Learning as a Design Material. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. ACM, 278–288.

Digital Library

[28]

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

Digital Library

[29]

Sarah Fdili Alaoui. 2019. Making an interactive dance piece: Tensions in integrating technology in art. In Proceedings of the 2019 on Designing Interactive Systems Conference. 1195–1208.

Digital Library

[30]

Sidney S Fels and Geoffrey E Hinton. 1993. Glove-talk: A neural network interface between a data-glove and a speech synthesizer. IEEE transactions on Neural Networks 4, 1 (1993), 2–8.

[31]

Rebecca Fiebrink, Perry R. Cook, and Dan Trueman. 2011. Human Model Evaluation in Interactive Supervised Learning. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Vancouver, BC, Canada) (CHI ’11). ACM, New York, NY, USA, 147–156. https://doi.org/10.1145/1978942.1978965

Digital Library

[32]

Rebecca Fiebrink and Laetitia Sonami. 2020. Reflections on Eight Years of Instrument Creation with Machine Learning. In Proceedings of the 20th International Conference on New Interfaces for Musical Expression (NIME’20).

[33]

Rebecca Fiebrink, Dan Trueman, and Perry R Cook. 2009. A Meta-Instrument for Interactive, On-the-Fly Machine Learning. In NIME. 280–285.

[34]

Jules Françoise and Frederic Bevilacqua. 2018. Motion-sound mapping through interaction: An approach to user-centered design of auditory feedback using machine learning. ACM Transactions on Interactive Intelligent Systems (TiiS) 8, 2(2018), 1–30.

Digital Library

[35]

Jules Françoise, Yves Candau, Sarah Fdili Alaoui, and Thecla Schiphorst. 2017. Designing for kinesthetic awareness: Revealing user experiences through second-person inquiry. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. 5171–5183.

Digital Library

[36]

Jules Françoise, Norbert Schnell, Riccardo Borghesi, and Frédéric Bevilacqua. 2014. Probabilistic models for designing motion and sound relationships. In Proceedings of the 2014 international conference on new interfaces for musical expression. 287–292.

[37]

Christopher Frauenberger. 2019. Entanglement HCI The Next Wave?ACM Transactions on Computer-Human Interaction (TOCHI) 27, 1(2019), 1–27.

[38]

Martin Friedmann, Thad Starner, and Alex Pentland. 1992. Device synchronization using an optimal linear filter. In Proceedings of the 1992 Symposium on Interactive 3D Graphics. 57–62.

Digital Library

[39]

Petra Gemeinboeck. 2020. The aesthetics of encounter: a relational-performative design approach to human-robot interaction. Frontiers in Robotics and AI 7 (2020), 217.

[40]

Petra Gemeinboeck and Rob Saunders. 2013. Creative Machine Performance: Computational Creativity and Robotic Art. In ICCC. 215–219.

[41]

Elisa Giaccardi and Elvin Karana. 2015. Foundations of materials experience: An approach for HCI. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems. 2447–2456.

Digital Library

[42]

Elisa Giaccardi, Chris Speed, Nazli Cila, and M Caldwell. 2016. Things as co-ethnographers: Implications of a thing perspective for design and anthropology. Design anthropological futures 235 (2016).

[43]

Marco Gillies, Rebecca Fiebrink, Atau Tanaka, Jérémie Garcia, Frederic 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. ACM, 3558–3565.

Digital Library

[44]

Ian Goodfellow, Yoshua Bengio, and Aaron Courville. 2016. Deep learning. MIT press.

Digital Library

[45]

Sabrina Hauser, Doenja Oogjes, Ron Wakkary, and Peter-Paul Verbeek. 2018. An annotated portfolio on doing postphenomenology through research products. In Proceedings of the 2018 designing interactive systems conference. 459–471.

Digital Library

[46]

Peter Henderson, Jieru Hu, Joshua Romoff, Emma Brunskill, Dan Jurafsky, and Joelle Pineau. 2020. Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning. arXiv preprint arXiv:2002.05651(2020).

[47]

Cher M Hill. 2017. More-than-reflective practice: Becoming a diffractive practitioner. Teacher Learning and Professional Development 2, 1(2017).

[48]

Kristina Höök. 2018. Designing with the body: somaesthetic interaction design. MIT Press.

[49]

Kristina Höök and Jonas Löwgren. 2012. Strong concepts: Intermediate-level knowledge in interaction design research. ACM Transactions on Computer-Human Interaction (TOCHI) 19, 3(2012), 23.

Digital Library

[50]

Laewoo Kang, Steven J Jackson, and Phoebe Sengers. 2018. Intermodulation: Improvisation and Collaborative Art Practice for HCI. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. 1–13.

Digital Library

[51]

Ashish Kapoor, Bongshin Lee, Desney Tan, and Eric Horvitz. 2010. Interactive optimization for steering machine classification. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. 1343–1352.

Digital Library

[52]

Chris Kiefer and Thor Magnusson. 2019. Live coding machine learning and machine listening: a survey on the design of languages and environments for live coding. In Proceedings of the International Conference on Live Coding. ICLC.

[53]

Matthew Lee and David Wessel. 1992. Connectionist models for real-time control of synthesis and compositional algorithms. In Proceedings of the International Computer Music Conference. 277–277.

[54]

Florent Levillain and Elisabetta Zibetti. 2017. Behavioral objects: The rise of the evocative machines. Journal of Human-Robot Interaction 6, 1 (2017), 4–24.

Digital Library

[55]

Golan Levin and Tega Brain. 2020. Code as Creative Medium: A Handbook for Computational Art and Design. MIT Press.

[56]

Wanyu Liu, Rafael Lucas d’Oliveira, Michel Beaudouin-Lafon, and Olivier Rioul. 2017. Bignav: Bayesian information gain for guiding multiscale navigation. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. ACM, 5869–5880.

Digital Library

[57]

Tod Machover. 1989. Hyperinstrument: Musically Intelligent and Interactive Performance and Creativity Systems. Proceedings of 1989 ICMC(1989), 186–190.

[58]

Nirav Malsattar, Tomo Kihara, and Elisa Giaccardi. 2019. Designing and Prototyping from the Perspective of AI in the Wild. In Proceedings of the 2019 on Designing Interactive Systems Conference. 1083–1088.

Digital Library

[59]

Benjamin Matuszewski, Joseph Larralde, and Frédéric Bevilacqua. 2018. Designing Movement Driven Audio Applications Using a Web-Based Interactive Machine Learning Toolkit. In Web Audio Conference (WAC).

[60]

Marek P Michalowski, Selma Sabanovic, and Hideki Kozima. 2007. A dancing robot for rhythmic social interaction. In Proceedings of the ACM/IEEE international conference on Human-robot interaction. 89–96.

Digital Library

[61]

Kevin P Murphy. 2012. Machine learning: a probabilistic perspective. MIT press.

Digital Library

[62]

Iohanna Nicenboim, Elisa Giaccardi, Marie Louise Juul Søndergaard, Anuradha Venugopal Reddy, Yolande Strengers, James Pierce, and Johan Redström. 2020. More-Than-Human Design and AI: In Conversation with Agents. In Companion Publication of the 2020 ACM Designing Interactive Systems Conference. 397–400.

Digital Library

[63]

Antti Oulasvirta. 2018. Computational interaction. Oxford University Press.

[64]

Allison Parrish. 2018. Articulations. Counterpath Press.

[65]

Simon Penny. 2017. Making sense: Cognition, computing, art, and embodiment. MIT Press.

[66]

Claudio S Pinhanez. 1996. Computer theater. In Proc. of the Eighth International Symposium on Electronic Arts (ISEA’97). Citeseer.

[67]

Ivan Poupyrev, Michael J Lyons, Sidney Fels, and Tina Blaine. 2001. New interfaces for musical expression. In CHI’01 Extended Abstracts on Human Factors in Computing Systems. 491–492.

[68]

Jane Prophet and Helen Pritchard. 2015. Diffractive art practices: Computation and the messy entanglements between mainstream contemporary art, and new media art. artnodes 15(2015).

[69]

Jane Prophet and Helen Pritchard. 2015. Performative apparatus and diffractive practices: An account of artificial life art. Artificial Life 21, 3 (2015), 332–343.

Digital Library

[70]

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. ACM, W11.

[71]

Cynthia Rudin. 2019. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence 1, 5 (2019), 206–215.

[72]

Téo Sanchez, Baptiste Caramiaux, Jules Françoise, Frédéric Bevilacqua, and Wendy Mackay. 2021. How do People Train a Machine? Strategies and (Mis)Understandings. In Proceedings of the 24th ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW). ACM.

[73]

Diemo Schwarz, Grégory Beller, Bruno Verbrugghe, and Sam Britton. 2006. Real-time corpus-based concatenative synthesis with catart. In 9th International Conference on Digital Audio Effects (DAFx). 279–282.

[74]

Hugo Scurto, Frédéric Bevilacqua, and Jules Françoise. 2017. Shaping and Exploring Interactive Motion-Sound Mappings Using Online Clustering Techniques. In Proceedings of the International Conference on New Interfaces for Musical Expression (NIME’17).

[75]

Hugo Scurto, Rebecca Fiebrink, 2016. Grab-and-play mapping: Creative machine learning approaches for musical inclusion and exploration. In Proceedings of the 2016 International Computer Music Conference.

[76]

Hugo Scurto, Bavo Van Kerrebroeck, Baptiste Caramiaux, and Frédéric Bevilacqua. 2021. Designing Deep Reinforcement Learning for Human Parameter Exploration. ACM Trans. Comput.-Hum. Interact. 28, 1, Article 1 (Jan. 2021), 35 pages. https://doi.org/10.1145/3414472

Digital Library

[77]

Richard S Sutton and Andrew G Barto. 2011. Reinforcement learning: An introduction. Cambridge, MA: MIT Press.

Digital Library

[78]

Hillevi Lenz Taguchi. 2012. A diffractive and Deleuzian approach to analysing interview data. Feminist theory 13, 3 (2012), 265–281.

[79]

Koray Tahiroglu, Miranda Kastemaa, and Oskar Koli. 2020. Al-terity: Non-Rigid Musical Instrument with Artificial Intelligence Applied to Real-Time Audio Synthesis. In Proceedings of the 2020 International Conference on New Interfaces for Musical Expression.

[80]

Koray Tahiroğlu, Thomas Svedström, and Valtteri Wikström. 2015. Noisa: A novel intelligent system facilitating smart interaction. In Proceedings of the 33rd annual acm conference extended abstracts on human factors in computing systems. 279–282.

Digital Library

[81]

Samuel Thompson, Hugo Scurto, and Rebecca Fiebrink. 2019. Sound Control: Supporting Custom Musical Interface Design for Children with Disabilities. In Proceedings of the 19th International Conference on New Interfaces for Musical Expression (NIME 2019).

[82]

Emanuel Todorov, Tom Erez, and Yuval Tassa. 2012. Mujoco: A physics engine for model-based control. In 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 5026–5033.

[83]

Elena Tosi Brandi. 2018. Design de l’expérience sensible: une philosophie des sens pour le design et la création. Ph.D. Dissertation. Paris Sciences et Lettres.

[84]

Nikolaus Troje, Francesca Simion, Lara Bardi, Elena Mascalzoni, Lucia Regolin, Emily Grossman, Maggie Shiffrar, James P Thomas, Phil McAleer, Scott Love, 2013. Social perception: Detection and interpretation of animacy, agency, and intention. MIT Press.

[85]

Anna Vallgårda. 2014. Giving form to computational things: developing a practice of interaction design. Personal and ubiquitous computing 18, 3 (2014), 577–592.

[86]

Kiri L Wagstaff. 2012. Machine Learning that Matters. In In Procs of ICML. Citeseer.

[87]

Danding Wang, Qian Yang, Ashraf Abdul, and Brian Y Lim. 2019. Designing theory-driven user-centric explainable AI. In Proceedings of the 2019 CHI conference on human factors in computing systems. 1–15.

Digital Library

[88]

Mikael Wiberg. 2014. Methodology for materiality: interaction design research through a material lens. Personal and ubiquitous computing 18, 3 (2014), 625–636.

[89]

Qian Yang. 2018. Machine learning as a UX design material: How can we imagine beyond automation, recommenders, and reminders?. In AAAI Spring Symposia.

[90]

Qian Yang, Nikola Banovic, and John Zimmerman. 2018. Mapping Machine Learning Advances from HCI Research to Reveal Starting Places for Design Innovation. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. ACM, 130.

Digital Library

[91]

Qian Yang, Alex Scuito, John Zimmerman, Jodi Forlizzi, and Aaron Steinfeld. 2018. Investigating How Experienced UX Designers Effectively Work with Machine Learning. In Proceedings of the 2018 Designing Interactive Systems Conference. ACM, 585–596.

Digital Library

[92]

Qian Yang, Jina Suh, Nan-Chen Chen, and Gonzalo Ramos. 2018. 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

[93]

Mo H Zareei, Dale A Carnegie, Ajay Kapur, and Dugal McKinnon. 2014. Mutor: Drone chorus of metrically muted motors. In Internation Computer Music Conference.

[94]

Michael Zbyszynski, Mick Grierson, Matthew Yee-King, 2017. Rapid prototyping of new instruments with codecircle. In Proceedings of the 17th International Conference on New Interfaces for Musical Expression.

Cited By

Arzberger ALupetti MGiaccardi E(2024)Reflexive Data Curation: Opportunities and Challenges for Embracing Uncertainty in Human-AI CollaborationACM Transactions on Computer-Human Interaction10.1145/3689042Online publication date: 22-Aug-2024
https://doi.org/10.1145/3689042
Bennett CShelby RRostamzadeh NKane S(2024)Painting with Cameras and Drawing with Text: AI Use in Accessible CreativityProceedings of the 26th International ACM SIGACCESS Conference on Computers and Accessibility10.1145/3663548.3675644(1-19)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3663548.3675644
Lee CLee MMutlu B(2024)The AI-DEC: A Card-based Design Method for User-centered AI ExplanationsProceedings of the 2024 ACM Designing Interactive Systems Conference10.1145/3643834.3661576(1010-1028)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3643834.3661576
Show More Cited By

Recommendations

A General Recipe for Automated Machine Learning in Practice
Advances in Artificial Intelligence – IBERAMIA 2022
Abstract
Automated Machine Learning (AutoML) is an area of research that focuses on developing methods to generate machine learning models automatically. The idea of being able to build machine learning models with very little human intervention represents ...
Wizard of Oz Prototyping for Machine Learning Experiences
CHI EA '19: Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems

Machine learning is being adopted in a wide range of products and services. Despite its adoption, design and research processes for machine learning experiences have yet to be cemented in the user experience community. Prototyping machine learning ...
Machine Learning: The State of the Art

The two fundamental problems in machine learning (ML) are statistical analysis and algorithm design. The former tells us the principles of the mathematical models that we establish from the observation data. The latter defines the conditions on which ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

DIS '21: Proceedings of the 2021 ACM Designing Interactive Systems Conference

June 2021

2082 pages

ISBN:9781450384766

DOI:10.1145/3461778

Editors:
Wendy Ju
Jacobs Technion-Cornell Institute at Cornell Tech
,
Lora Oehlberg
University of Calgary
,
Sean Follmer
Stanford University
,
Sarah Fox
Carnegie Mellon University
,
Stacey Kuznetsov
Arizona State University

Copyright © 2021 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 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

SIGCHI: ACM Special Interest Group on Computer-Human Interaction

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 June 2021

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

DIS '21

Sponsor:

SIGCHI

DIS '21: Designing Interactive Systems Conference 2021

June 28 - July 2, 2021

Virtual Event, USA

Acceptance Rates

Overall Acceptance Rate 1,158 of 4,684 submissions, 25%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

19
Total Citations
View Citations
488
Total Downloads

Downloads (Last 12 months)92
Downloads (Last 6 weeks)12

Reflects downloads up to 10 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Arzberger ALupetti MGiaccardi E(2024)Reflexive Data Curation: Opportunities and Challenges for Embracing Uncertainty in Human-AI CollaborationACM Transactions on Computer-Human Interaction10.1145/3689042Online publication date: 22-Aug-2024
https://doi.org/10.1145/3689042
Bennett CShelby RRostamzadeh NKane S(2024)Painting with Cameras and Drawing with Text: AI Use in Accessible CreativityProceedings of the 26th International ACM SIGACCESS Conference on Computers and Accessibility10.1145/3663548.3675644(1-19)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3663548.3675644
Lee CLee MMutlu B(2024)The AI-DEC: A Card-based Design Method for User-centered AI ExplanationsProceedings of the 2024 ACM Designing Interactive Systems Conference10.1145/3643834.3661576(1010-1028)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3643834.3661576
Sivertsen CLøvlie A(2024)Exploring Aesthetic Qualities of Deep Generative Models through Technological (Art) MediationProceedings of the 2024 ACM Designing Interactive Systems Conference10.1145/3643834.3661498(2738-2752)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3643834.3661498
Shelby RSrinivasan RBurgdorf KLena JRostamzadeh N(2024)Creative ML Assemblages: The Interactive Politics of People, Processes, and ProductsProceedings of the ACM on Human-Computer Interaction10.1145/36373158:CSCW1(1-30)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3637315
Sivertsen CSalimbeni GLøvlie ABenford SZhu J(2024)Machine Learning Processes As Sources of Ambiguity: Insights from AI ArtProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642855(1-14)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642855
Shelby RRismani SRostamzadeh N(2024)Generative AI in Creative Practice: ML-Artist Folk Theories of T2I Use, Harm, and Harm-ReductionProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642461(1-17)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642461
Morrison LMcPherson A(2024)Entangling Entanglement: A Diffractive Dialogue on HCI and Musical InteractionsProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642171(1-17)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642171
He QZheng WBao HChen RTong X(2023)Exploring Designers’ Perceptions and Practices of Collaborating with Generative AI as a Co-creative Agent in a Multi-stakeholder Design Process: Take the Domain of Avatar Design as an ExampleProceedings of the Eleventh International Symposium of Chinese CHI10.1145/3629606.3629675(596-613)Online publication date: 13-Nov-2023
https://dl.acm.org/doi/10.1145/3629606.3629675
Delgado FYang SMadaio MYang Q(2023)The Participatory Turn in AI Design: Theoretical Foundations and the Current State of PracticeProceedings of the 3rd ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization10.1145/3617694.3623261(1-23)Online publication date: 30-Oct-2023
https://dl.acm.org/doi/10.1145/3617694.3623261
Show More Cited By

View Options

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 Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View Table of Contents