Zhibin Zhou
ABSTRACT
Machine learning (ML) is now widely used to empower products and services, but there is a lack of research on the tools that involve designers in the entire ML process. Thus, designers who are new to ML technology may struggle to fully understand the capabilities of ML, users, and scenarios when designing ML-empowered products. This paper describes a design tool, ML-Process Canvas, which assists designers in considering the specific factors of the user, ML system, and scenario throughout the whole ML process. The Canvas was applied to a design project, and was observed to contribute in the conceptual phase of UX design practice. In the future, we hope that the ML-Process Canvas will become more practical through continued use in design practice.
Supplemental Material
- 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), 105--120.Google Scholar
Digital Library
- Haydee M Cuevas, Stephen M Fiore, Barrett S Caldwell, and Strater Laura. 2007. Augmenting team cognition in human-automation teams performing in complex operational environments. Aviation Space & Environmental Medicine, 78(5), B63-B70.Google Scholar
- 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, Denver, Colorado, USA, 278--288. Google ScholarDigital Library
- Jodi Forlizzi, John Zimmerman, Vince Mancuso, and Sonya Kwak. 2007. How interface agents affect interaction between humans and computers. In Proceedings of the 2007 conference on Designing pleasurable products and interfaces. ACM, 209--221. Google ScholarDigital Library
- Peter A. Hancock and Mark H. Chignell. 1988. Mental workload dynamics in adaptive interface design. IEEE Transactions on Systems Man & Cybernetics, 18(4), 647--658.Google ScholarCross Ref
- Lars Erik Holmquist. 2017. Intelligence on tap: artificial intelligence as a new design material. Interactions, 24(4), 28--33. Google ScholarDigital Library
- Tharon Howard. 2014. Journey mapping: a brief overview. Commun. Des. Q. Rev, 2(3), 10--13. Google ScholarDigital Library
- Yung-Ming Li and Yung-Shao Yeh. 2010. Increasing trust in mobile commerce through design aesthetics. Comput. Hum. Behav., 26(4), 673--684. Google ScholarDigital Library
- Stephanie M Merritt, Heimbaugh Heather, La Chapell Jennifer, and Lee Deborah. 2013. I trust it, but I don't know why: effects of implicit attitudes toward automation on trust in an automated system. Human Factors, 55(3), 520--534.Google ScholarCross Ref
- Kristin E Schaefer, Jessie YC Chen, James L Szalma, and Peter A Hancock. 2016. A meta-analysis of factors influencing the development of trust in automation: Implications for understanding autonomy in future systems. Human factors, 58(3), 377--400.Google Scholar
- Molly Wood. Hopes to Serve Up Music in a Novel Way. Retrieved December 27, 2018 from http://nyti.ms/1fr8LY5Google Scholar
- 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, Montreal QC, Canada, Paper No. 130. Google ScholarDigital Library
- 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, Hong Kong, China, 585--596. Google ScholarDigital Library
- Qian Yang, John Zimmerman, Aaron Steinfeld, and Anthony Tomasic. 2016. Planning Adaptive Mobile Experiences When Wireframing. In Proceedings of the 2016 ACM Conference on Designing Interactive Systems. ACM, Brisbane, QLD, Australia, 565--576. Google ScholarDigital Library
Index Terms
- ML-Process Canvas: A Design Tool to Support the UX Design of Machine Learning-Empowered Products
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