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Co-design of a trustworthy AI system in healthcare: deep learning based skin lesion classifier
Frankfurt Big Data Lab, Goethe University Frankfurt, Frankfurt, Germany; Department of Business Management and Analytics, Arcada University of Applied Sciences, Helsinki, Finland; Data Science Graduate School, Seoul National University, Seoul, South Korea.
German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany.
Health Ethics and Policy Lab, Swiss Federal Institute of Technology (ETH Zurich), Zurich, Switzerland.
Department of Dermatology, University Clinic Münster, Münster, Germany; Department of Dermatology, Medical Faculty, Heinrich-Heine University, Düsseldorf, Germany.
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2021 (Engelska)Ingår i: Frontiers in Human Dynamics, E-ISSN 2673-2726 , Vol. 3, artikel-id 688152Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

This paper documents how an ethically aligned co-design methodology ensures trustworthiness in the early design phase of an artificial intelligence (AI) system component for healthcare. The system explains decisions made by deep learning networks analyzing images of skin lesions. The co-design of trustworthy AI developed here used a holistic approach rather than a static ethical checklist and required a multidisciplinary team of experts working with the AI designers and their managers. Ethical, legal, and technical issues potentially arising from the future use of the AI system were investigated. This paper is a first report on co-designing in the early design phase. Our results can also serve as guidance for other early-phase AI-similar tool developments.

Ort, förlag, år, upplaga, sidor
Frontiers Media S.A., 2021. Vol. 3, artikel-id 688152
Nyckelord [en]
artificial intelligence, ethical co-design, ethics, healthcare, malignant melanoma, trustworthy AI, trustworthy AI Co-design, Z-inspection®1
Nationell ämneskategori
Systemvetenskap, informationssystem och informatik
Identifikatorer
URN: urn:nbn:se:umu:diva-217550DOI: 10.3389/fhumd.2021.688152ISI: 001097602600001Scopus ID: 2-s2.0-85121899656OAI: oai:DiVA.org:umu-217550DiVA, id: diva2:1818584
Forskningsfinansiär
EU, Horisont 2020, 101016233Deutsche Forschungsgemeinschaft (DFG), EXC 2064/1Tillgänglig från: 2023-12-11 Skapad: 2023-12-11 Senast uppdaterad: 2023-12-11Bibliografiskt granskad

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Campano, Erik

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