Abstract
Artificial Intelligence (AI) has provided user experience (UX) designers with a richer toolset. To use technologies such as Machine Learning (ML) that can expand their creative capacity to design intelligent services. ML has the capability to enhance the user experience, for example, by improving efficiency, personalization, and context-aware adaptation. However, research suggests ML as a challenging design material in UX practice, such as difficulties in comprehending data dependencies when prototyping, or the lack of tools and methods for evaluating adaptive user experiences. Previous research indicates that lack of knowledge transfer into the UX design practice may hamper innovative potential. This work aims to provide new insights on how designers think about – and experience – design for AI-powered services. It is important to make ML-powered services beneficial and sustainable for end-users, organizations, and society. Therefore, we explore UX designers’ reflections and experiences of using ML in a design context. We have performed nine deep explorative interviews with professional designers that work with ML. The respondents have different backgrounds, seniority, and work in different sectors. The collected interview material was qualitatively analyzed and resulted in five conceptual themes for how UX designers experience the design context surrounding AI-powered services: 1) Absence of competence, 2) Lack of incentive for competence development, 3) Challenges in articulating design criteria, 4) Mature vs. Immature clients, and 5) Lack of support for ethical concerns. We provide implications for how these themes affect the design context and practice.
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References
Amershi, S., et al.: Guidelines for human-AI interaction. In: CHI 2019, Glasgow, pp. 1–13 (2019)
Braun, V., Clarke, V.: Using thematic analysis in psychology. Qual. Res. Psychol. 3, 77–101 (2006)
Courage, C., Baxter, K.: Understanding Your Users: A Practical Guide to User Requirements Methods, Tools, and Techniques, Gulf Professional Publishing, Amsterdam (2005)
Cooper, A., Reimann, R., Cronin, D., Noessel, C.: About Face: The Essentials of Interaction Design. Wiley, Indianapolis (2014)
Dove, G., Halskov, K., Forlizzi, J., Zimmerman, J.: UX design innovation: challenges for working with machine learning as a design material. In: CHI 2017, Denver, pp. 278–288 (2017)
Cross, N.: Designerly ways of knowing. Des. Stud. 3(4), 221–227 (1982)
Holmquist, L.E.: Intelligence on tap: artificial intelligence as a new design material. Interactions 24, 28–33 (2017)
Löwgren, J.: Interaction Design - brief intro, The encyclopedia of human-computer interaction, The Interaction Design Foundation (2012). https://www.interaction-design.org/literature/book/the-encyclopedia-of-human-computer-interaction-2nd-ed/interaction-design-brief-intro
Löwgren, J., Stolterman, E.: Design av informationsteknik: Materialet utan egenskaper, Studentlitteratur AB, Lund (2004)
Komischke, T.: Human-centered artificial intelligence considerations and implementations: a case study from software product development. In: Degen, H., Ntoa, S. (eds.) HCII 2021. LNCS, vol. 12797, pp. 260–268. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-77772-2_17
Konstan, J.A., Riedl, J.: Recommender systems: from algorithms to user experience. User Model User Adapt. Interact. 22, pp. 101–123 (2012)
Myers, M.D., Newman, M.: The qualitative interview in IS research: examining the craft. Inf. Organ. 17, pp. 2–26 (2007)
Getto, G., Beecher, F.: Toward a model of UX education: training UX designers within the academy. IEEE Trans. Prof. Commun. 59, 153–164 (2016)
Goodman, E., Stolterman, E., Wakkary, R.: Understanding interaction design practices. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1061–1070 (2011)
Stappers, P.J., Giaccardi, E.: Research through design. In: The Encyclopedia of Human-Computer Interaction, The Interaction Design Foundation (2017). https://www.interaction-design.org/literature/book/the-encyclopedia-of-human-computer-interaction-2nd-ed/research-through-design
Rittel, H.W., Webber, M.M.: Dilemmas in a general theory of planning. Policy Sci. 4, 155–169 (1973)
Xu, W.: Toward human-centered AI: a perspective from human-computer interaction. Interactions 26, 42–46 (2019)
Yang, Q., Zimmerman, J., Steinfeld, A., Tomasic, A.: Planning adaptive mobile experiences when wireframing. In: DIS 2016, Brisbane, pp. 565–576 (2016)
Yang, Q.: Machine learning as a ux design material: how can we imagine beyond automation, recommenders, and reminders? In: AAAI 2018, Pennsylvania, March 2018
Yang, Q., Banovic, N., Zimmerman, J.: Mapping machine learning advances from HCI research to reveal starting places for design innovation. In: CHI 2018, Montréal, pp. 1–11, April 2018
Yang, Q., Scuito, A., Zimmerman, J., Forlizzi, J., Steinfeld, A.: Investigating how experienced UX designers effectively work with machine learning. In: DIS 2018, Hong Kong, pp. 585–596, June 2018
Yang, Q., Steinfeld, A., Rosé, C., Zimmerman, J.: Re-examining whether, why, and how human-AI interaction is uniquely difficult to design. In: CHI 202, Honolulu, pp. 1–13, April 2020
Fernaeus, Y., Sundström, P.: The material move how materials matter in interaction design research. In: DIS 2012, Newcastle, pp. 486–495 (2012)
Zhu, J., Liapis, A., Risi, S., Bidarra, R., Youngblood, G.M.: Explainable AI for designers: A human-centered perspective on mixed-initiative co-creation. In: IEEE 2018 (CIG), Maastricht, pp. 1–8 (2018)
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Bergström, E., Wärnestål, P. (2022). Exploring the Design Context of AI-Powered Services: A Qualitative Investigation of Designers’ Experiences with Machine Learning. In: Degen, H., Ntoa, S. (eds) Artificial Intelligence in HCI. HCII 2022. Lecture Notes in Computer Science(), vol 13336. Springer, Cham. https://doi.org/10.1007/978-3-031-05643-7_1
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