Designing for machine learning —Investigating UX design practice in medical AI development

dc.contributorAalto Universityen
dc.contributorAalto-yliopistofi
dc.contributor.advisorLucero, Andrés
dc.contributor.advisorZhang, Michelle
dc.contributor.authorHao, Chengxin
dc.contributor.departmentDepartment of Designen
dc.contributor.departmentMuotoilun laitosfi
dc.contributor.schoolTaiteiden ja suunnittelun korkeakoulufi
dc.contributor.schoolSchool of Arts, Design and Architectureen
dc.contributor.supervisorLucero, Andrés
dc.date.accessioned2019-07-14T17:02:30Z
dc.date.available2019-07-14T17:02:30Z
dc.date.issued2019
dc.description.abstractMedical 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.en
dc.format.extent127
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/39288
dc.identifier.urnURN:NBN:fi:aalto-201907144352
dc.language.isoenen
dc.locationP1 OPINNÄYTTEET D 2019 Hao
dc.programmeCollaborative and Industrial Designen
dc.subject.keyworduser experience designen
dc.subject.keywordUX practiceen
dc.subject.keywordmachine learningen
dc.subject.keywordmedical AIen
dc.subject.keyworddesign materialen
dc.subject.keywordcross-disciplinary collaborationen
dc.titleDesigning for machine learning —Investigating UX design practice in medical AI developmenten
dc.typeG2 Pro gradu, diplomityöfi
dc.type.ontasotMaster's thesisen
dc.type.ontasotMaisterin opinnäytefi
local.aalto.barcode1210015686

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