Accountability, Transparency and Explainability in AI for Healthcare

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dc.contributor.authorMoltubakk Kempton, Alexander
dc.contributor.authorVassilakopoulou, Polyxeni
dc.date.accessioned2021-09-19T19:14:07Z
dc.date.available2021-09-19T19:14:07Z
dc.date.issued2021
dc.description.abstractThe multiplicity of actors and the opacity of technologies involved in data management, algorithm crafting and systems ́ development for the deployment of Artificial Intelligence (AI) in healthcare create governance challenges. This study analyzes extant AI governance research in the context of healthcare focusing on accountability, transparency and explainability. We find that a significant part of this body of research lacks conceptual clarity and that the relationship between accountability, transparency and explainability is not fully explored. We also find that papers written back in the 1980s, identify and discuss many of the issues that are currently discussed. Up to today, most published research is only conceptual and brings contributions in the form of frameworks and guidelines that need to be further investigated empirically.en
dc.identifier.doi10.18420/ihc2021_018
dc.identifier.pissn2510-2591
dc.identifier.urihttps://dl.eusset.eu/handle/20.500.12015/4186
dc.language.isoen
dc.publisherEuropean Society for Socially Embedded Technologies (EUSSET)
dc.relation.ispartofInfrahealth 2021 - Proceedings of the 8th International Conference on Infrastructures in Healthcare
dc.relation.ispartofseriesReports of the European Society for Socially Embedded Technologies: vol. 5, no. 4
dc.titleAccountability, Transparency and Explainability in AI for Healthcareen
dc.typeText/Conference Paper
gi.conference.date23-24 September 2021
gi.conference.locationKristiansand, Norway

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