Designing for machine learning —Investigating UX design practice in medical AI development
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School of Arts, Design and Architecture |
Master's thesis
Ask about the availability of the thesis by sending email to the Aalto University Learning Centre oppimiskeskus@aalto.fi
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P1 OPINNÄYTTEET D 2019 Hao
Authors
Date
2019
Department
Major/Subject
Mcode
Degree programme
Collaborative and Industrial Design
Language
en
Pages
127
Series
Abstract
Medical artificial intelligence products in China are now experiencing rapid growth as a solution to critical drawbacks within the medical system. While this includes support from governmental policies, contributions from different disciplines are crucial. However, designing for AI is not, as yet, a thoroughly investigated topic within the design community. Through interviewing and observing the UX designers and data scientists working within organizations, the thesis studied the current design practices when designing for AI-enabled products, aiming to unveil the challenges when UX designers leverage artificial intelligence, to envisage the possible solutions to address the problems, and to elicit the implications for preparing UX designers and UX designers-to-be to proactively participate in the ML-related projects. The perceived challenges include understanding machine learning as design material, fulfilling the needs of the medical customers and users, and collaborating with data scientists. In addressing the given challenges, the work proposes a framework for building a project-specific understanding of the technology and establishes a procedural knowledge of the dynamics within the current collaboration between designers and data scientists based on the human-centered design process. The thesis also advocates for specific curriculums in design academies and more designer-friendly materials related to machine learning in order to push the technical boundaries towards a more human-centered focus within the technology-dominant discussion. Further research is needed to explore the optimal dynamics within the cross-disciplinary teams to achieve innovative design outcomes utilizing machine learning.Description
Supervisor
Lucero, AndrésThesis advisor
Lucero, AndrésZhang, Michelle
Keywords
user experience design, UX practice, machine learning, medical AI, design material, cross-disciplinary collaboration