Designing for machine learning —Investigating UX design practice in medical AI development
dc.contributor | Aalto University | en |
dc.contributor | Aalto-yliopisto | fi |
dc.contributor.advisor | Lucero, Andrés | |
dc.contributor.advisor | Zhang, Michelle | |
dc.contributor.author | Hao, Chengxin | |
dc.contributor.department | Department of Design | en |
dc.contributor.department | Muotoilun laitos | fi |
dc.contributor.school | Taiteiden ja suunnittelun korkeakoulu | fi |
dc.contributor.school | School of Arts, Design and Architecture | en |
dc.contributor.supervisor | Lucero, Andrés | |
dc.date.accessioned | 2019-07-14T17:02:30Z | |
dc.date.available | 2019-07-14T17:02:30Z | |
dc.date.issued | 2019 | |
dc.description.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. | en |
dc.format.extent | 127 | |
dc.identifier.uri | https://aaltodoc.aalto.fi/handle/123456789/39288 | |
dc.identifier.urn | URN:NBN:fi:aalto-201907144352 | |
dc.language.iso | en | en |
dc.location | P1 OPINNÄYTTEET D 2019 Hao | |
dc.programme | Collaborative and Industrial Design | en |
dc.subject.keyword | user experience design | en |
dc.subject.keyword | UX practice | en |
dc.subject.keyword | machine learning | en |
dc.subject.keyword | medical AI | en |
dc.subject.keyword | design material | en |
dc.subject.keyword | cross-disciplinary collaboration | en |
dc.title | Designing for machine learning —Investigating UX design practice in medical AI development | en |
dc.type | G2 Pro gradu, diplomityö | fi |
dc.type.ontasot | Master's thesis | en |
dc.type.ontasot | Maisterin opinnäyte | fi |
local.aalto.barcode | 1210015686 |