Hostname: page-component-77c89778f8-m42fx Total loading time: 0 Render date: 2024-07-16T13:25:35.888Z Has data issue: false hasContentIssue false
  • English
  • Français

AI VS. HUMAN: THE PUBLIC'S PERCEPTIONS OF THE DESIGN ABILITIES OF ARTIFICIAL INTELLIGENCE

Published online by Cambridge University Press:  19 June 2023

Leah Chong  and
Maria Yang
Leah Chong*
Affiliation:
Massachusetts Institute of Technology;
Maria Yang
Affiliation:
Massachusetts Institute of Technology
*
Chong, Leah, Massachusetts Institute of Technology, United States of America, leahmchong@gmail.com

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

With the increasing implementation of artificial intelligence (AI) in the design process, it is crucial to understand how users will accept AI-designed products. This work studies how the public currently perceives an AI's design capability as compared to a human designer's capability by conducting an online survey of 205 people via Amazon Mechanical Turk. The survey collects the respondents' perception on 16 specific bicycle design goals, demographic information, and self-reported level of design and AI/ML knowledge. Findings reveal that people think an AI would perform worse than a human designer on most design goals, particularly the goals that are user-dependent. This work also shows that the higher people's self-reported level of knowledge in design and the older they are, the more likely they are to think an AI's design capability would exceed a human designer's capability. The insights from this work add to the understanding of user acceptance of AI-designed products, as well as human designers' acceptance of AI input in human-AI teams.

Keywords

Artificial intelligenceCollaborative designMarket implications
Type
Article
Information
Proceedings of the Design Society , Volume 3: ICED23 , July 2023 , pp. 495 - 504
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2023. Published by Cambridge University Press

References

Alcaide-Marzal, J., Diego-Mas, J.A. and Acosta-Zazueta, G. (2020), “A 3D shape generative method for aesthetic product design”, Design Studies, Vol. 66, pp. 144–176. https://doi.org/10.1016/j.destud.2019.11.003CrossRefGoogle Scholar
Arning, K. and Ziefle, M. (2009), “Different perspectives on technology acceptance: The role of technology type and age”, Lecture Notes in Computer Science, Vol. 5889 LNCS, pp. 20–41. https://doi.org/10.1007/978-3-642-10308-7_2CrossRefGoogle Scholar
Camburn, B., Arlitt, R., Anderson, D., Sanaei, R., Raviselam, S., Jensen, D. and Wood, K.L. (2020), “Computer-aided mind map generation via crowdsourcing and machine learning”, Research in Engineering Design, Vol. 31 No. 4, pp. 383–409. https://doi.org/10.1007/s00163-020-00341-wCrossRefGoogle Scholar
Chen, Q., Wang, J., Pope, P., (Wayne) Chen, W. and Fuge, M. (2022), “Inverse Design of Two-Dimensional Airfoils Using Conditional Generative Models and Surrogate Log-Likelihoods”, Journal of Mechanical Design, Vol. 144 No. 2, https://doi.org/10.1115/1.4052846CrossRefGoogle Scholar
Davis, F.D. (1989), “Perceived usefulness, perceived ease of use, and user acceptance of information technology”, MIS Quarterly: Management Information Systems, Vol. 13 No. 3, pp. 319–339. https://doi.org/10.2307/249008CrossRefGoogle Scholar
Eppinger, S.D., Whitney, D.E., Smith, R.P. and Gebala, D.A. (1991), “Organizing the tasks in complex design projects”, Lecture Notes in Computer Science, Vol. 492 LNCS, pp. 229–252. https://doi.org/10.1007/BFb0014281CrossRefGoogle Scholar
Giacomin, J. (2015), “What Is Human Centred Design?”, The Design Journal, Vol. 17 No. 4, pp. 606–623. https://doi.org/10.2752/175630614X14056185480186CrossRefGoogle Scholar
Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K. and Cagan, J. (2022), “Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design”, Journal of Mechanical Design, Vol. 144 No. 2. https://doi.org/10.1115/1.4052488CrossRefGoogle Scholar
Hauk, N., Hüffmeier, J. and Krumm, S. (2018), “Ready to be a Silver Surfer? A Meta-analysis on the Relationship Between Chronological Age and Technology Acceptance”, Computers in Human Behavior, Vol. 84, pp. 304–319. https://doi.org/10.1016/j.chb.2018.01.020CrossRefGoogle Scholar
Hoffman, R.R., Johnson, M., Bradshaw, J.M. and Underbrink, A. (2013), “Trust in automation”, IEEE Intelligent Systems, Vo. 28 No. 1, pp. 84–88. https://doi.org/10.1109/MIS.2013.24CrossRefGoogle Scholar
Homburg, C., Schwemmle, M. and Kuehnl, C. (2015), “New Product Design: Concept, Measurement, and Consequences”, Journal of Marketing Research, Vol. 79 No. 3, pp. 41–56. https://doi.org/10.1509/jm.14.0199CrossRefGoogle Scholar
Hsu, S.H., Chuang, M.C. and Chang, C.C. (2000), “A semantic differential study of designers’ and users’ product form perception”, International Journal of Industrial Ergonomics, Vol. 25 No. 4, pp. 375–391. https://doi.org/10.1016/S0169-8141(99)00026-8CrossRefGoogle Scholar
Jang, S., Yoo, S. and Kang, N. (2022), “Generative Design by Reinforcement Learning: Enhancing the Diversity of Topology Optimization Designs”, Computer-Aided Design, Vol. 146, p. 103225. https://doi.org/10.1016/j.cad.2022.103225CrossRefGoogle Scholar
Jansson, D.G. and Smith, S.M. (1991), “Design fixation”, Design Studies, Vol. 12 No. 1, pp. 3–11. https://doi.org/10.1016/0142-694X(91)90003-FCrossRefGoogle Scholar
Kudrowitz, B.M. and Wallace, D. (2013), “Assessing the quality of ideas from prolific, early-stage product ideation”, Journal of Engineering Design, Vol. 24 No. 2, pp. 120–139. https://doi.org/10.1080/09544828.2012.676633CrossRefGoogle Scholar
Lan, L., Kannan, P.K. and Ratchford, B.T. (2008), “Incorporating Subjective Characteristics in Product Design and Evaluations”, Journal of Marketing Research, Vol. 45 No. 2, pp. 182–194. https://doi.org/10.1509/jmkr.45.2.1Google Scholar
Lee, J.D. and See, K.A. (2004), “Trust in automation: Designing for appropriate reliance”, Human Factors, Vol. 46 No. 1, pp. 50–80. https://doi.org/10.1518/hfes.46.1.50_30392CrossRefGoogle Scholar
Lee, J.D., Wickens, C.D., Liu, Y. and Boyle, L.N. (2017), Designing for People: An Introduction to Human Factors Engineering, CreateSpace Independent Publishing Platform, New York.Google Scholar
Maier, J.R.A. and Fadel, G.M. (2006), “Understanding the complexity of design”, In: Braha, D., Minai, A.A. and Bar-Yam, Y., Complex Engineered Systems, Springer, pp. 122–140. https://doi.org/10.1007/3-540-32834-3CrossRefGoogle Scholar
Mazé, F. and Ahmed, F. (2022), “TopoDiff: A Performance and Constraint-Guided Diffusion Model for Topology Optimization”, arXiv, https://doi.org/10.48550/arxiv.2208.09591CrossRefGoogle Scholar
McDonald, M.D. and McLaughlin, A.C. (2021), “Metrics of Comfort: Development of Physiological Correlates to Subjective Responses”, Proceedings of the Human Factors and Ergonomics Society Annual Meeting, Vol. 64 No. 1, pp. 1841–1845. https://doi.org/10.1177/1071181320641443CrossRefGoogle Scholar
Miaskiewicz, T. and Kozar, K.A. (2011), “Personas and user-centered design: How can personas benefit product design processes?”, Design Studies, Vol. 32 No. 5, pp. 417–430. https://doi.org/10.1016/j.destud.2011.03.003CrossRefGoogle Scholar
Nie, Z., Lin, T., Jiang, H. and Kara, L.B. (2021), “TopologyGAN: Topology optimization using generative adversarial networks based on physical fields over the initial domain”, Journal of Mechanical Design, Vol. 143 No. 3, pp. 031715. https://doi.org/10.1115/1.4049533/1094063CrossRefGoogle Scholar
Oh, S., Jung, Y., Kim, S., Lee, I. and Kang, N. (2019), “Deep generative design: Integration of topology optimization and generative models”, Journal of Mechanical Design, Vol. 141 No. 11, https://doi.org/10.1115/1.4044229CrossRefGoogle Scholar
Pan, I., Mason, L.R. and Matar, O.K. (2022), “Data-centric Engineering: integrating simulation, machine learning and statistics. Challenges and opportunities”, Chemical Engineering Science, Vol. 249, p. 117271. https://doi.org/10.1016/j.ces.2021.117271CrossRefGoogle Scholar
Paramasivam, V. and Senthil, V. (2009), “Analysis and evaluation of product design through design aspects using digraph and matrix approach”, International Journal on Interactive Design and Manufacturing, Vol. 3 No. 1, pp. 13–23. https://doi.org/10.1007/s12008-009-0057-9CrossRefGoogle Scholar
Raina, A., Mccomb, C. and Cagan, J. (2019), “Learning to Design From Humans: Imitating Human Designers Through Deep Learning”, Journal of Mechanical Design, Vol. 141 No. 11, pp. 111102. https://doi.org/10.1115/1.4044256CrossRefGoogle Scholar
Regenwetter, L., Nobari, A.H. and Ahmed, F. (2022). “Deep Generative Models in Engineering Design: A Review”, Journal of Mechanical Design, Vol. 144 No. 7, pp. 071704. https://doi.org/10.1115/1.4053859CrossRefGoogle Scholar
Shah, J.J., Smith, S.M. and Vargas-Hernandez, N. (2003), “Metrics for measuring ideation effectiveness”, Design Studies, Vol. 24 No. 2, pp. 111–134. https://doi.org/10.1016/S0142-694X(02)00034-0CrossRefGoogle Scholar
Shu, D., Cunningham, J., Stump, G., Miller, S.W., Yukish, M.A., Simpson, T.W. and Tucker, C.S. (2020), “3D Design Using Generative Adversarial Networks and Physics-Based Validation”, Journal of Mechanical Design, Vol. 142 No. 7, pp. 071701. https://doi.org/10.1115/1.4045419/1067306CrossRefGoogle Scholar
Song, B., Miller, S. and Ahmed, F. (2022), “Hey, AI! Can You See What I See? Multimodal Transfer Learning-Based Design Metrics Prediction for Sketches With Text Descriptions”, Proceedings of the ASME 2022 International Design Engineering Technical Conference, St. Louis, Missouri, August 14–17, 2022, ASME Digital Collection. https://doi.org/10.1115/DETC2022-91269CrossRefGoogle Scholar
Landauer, T.K. (1996), The Trouble with Computers: Usefulness, Usability, and Productivity, The MIT Press, Cambridge, MA. https://doi.org/10.7551/MITPRESS/6918.003.0015CrossRefGoogle Scholar
Williams, G., Meisel, N.A., Simpson, T.W. and McComb, C. (2019), “Design repository effectiveness for 3D convolutional neural networks: Application to additive manufacturing”, Journal of Mechanical Design, Vol. 141 No. 11, pp. 111701. https://doi.org/10.1115/1.4044199/955346CrossRefGoogle Scholar
Zhang, W., Yang, Z., Jiang, H., Nigam, S., Yamakawa, S., Furuhata, T., Shimada, K., Kara, L.B. (2019), “3D Shape Synthesis for Conceptual Design and Optimization Using Variational Autoencoders”, Proceedings of the ASME Design Engineering Technical Conference, Anaheim, California, August 18-21, 2019, ASME Digital Collection. https://doi.org/10.1115/DETC2019-98525CrossRefGoogle Scholar