Fecho, Mariska ; Zöll, Anne (2023)
The Power of Trust: Designing Trustworthy Machine Learning Systems in Healthcare.
International Conference on Information Systems. Hyderabad, India (2023, December 10-13)
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
Machine Learning (ML) systems have an enormous potential to improve medical care, but skepticism about their use persists. Their inscrutability is a major concern which can lead to negative attitudes reducing end users trust and resulting in rejection. Consequently, many ML systems in healthcare suffer from a lack of user-centricity. To overcome these challenges, we designed a user-centered, trustworthy ML system by applying design science research. The design includes meta-requirements and design principles instantiated by mockups. The design is grounded on our kernel theory, the Trustworthy Artificial Intelligence principles. In three design cycles, we refined the design through focus group discussions (N1=8), evaluation of existing applications, and an online survey (N2=40). Finally, an effectiveness test was conducted with end users (N3=80) to assess the perceived trustworthiness of our design. The results demonstrated that the end users did indeed perceive our design as more trustworthy.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2023 |
| Autor(en): | Fecho, Mariska ; Zöll, Anne |
| Art des Eintrags: | Bibliographie |
| Titel: | The Power of Trust: Designing Trustworthy Machine Learning Systems in Healthcare |
| Sprache: | Englisch |
| Publikationsjahr: | 10 Dezember 2023 |
| Ort: | Proceedings of the International Conference on Information Systems |
| Veranstaltungstitel: | International Conference on Information Systems |
| Veranstaltungsort: | Hyderabad, India |
| Veranstaltungsdatum: | 2023, December 10-13 |
| Zugehörige Links: | |
| Kurzbeschreibung (Abstract): | Machine Learning (ML) systems have an enormous potential to improve medical care, but skepticism about their use persists. Their inscrutability is a major concern which can lead to negative attitudes reducing end users trust and resulting in rejection. Consequently, many ML systems in healthcare suffer from a lack of user-centricity. To overcome these challenges, we designed a user-centered, trustworthy ML system by applying design science research. The design includes meta-requirements and design principles instantiated by mockups. The design is grounded on our kernel theory, the Trustworthy Artificial Intelligence principles. In three design cycles, we refined the design through focus group discussions (N1=8), evaluation of existing applications, and an online survey (N2=40). Finally, an effectiveness test was conducted with end users (N3=80) to assess the perceived trustworthiness of our design. The results demonstrated that the end users did indeed perceive our design as more trustworthy. |
| Fachbereich(e)/-gebiet(e): | 01 Fachbereich Rechts- und Wirtschaftswissenschaften 01 Fachbereich Rechts- und Wirtschaftswissenschaften > Betriebswirtschaftliche Fachgebiete 01 Fachbereich Rechts- und Wirtschaftswissenschaften > Betriebswirtschaftliche Fachgebiete > Wirtschaftsinformatik 01 Fachbereich Rechts- und Wirtschaftswissenschaften > Betriebswirtschaftliche Fachgebiete > Fachgebiet Software Business & Information Management |
| Hinterlegungsdatum: | 27 Nov 2023 09:14 |
| Letzte Änderung: | 27 Nov 2023 09:14 |
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