Lessons Learned from Assessing Trustworthy AI in Practice
Dennis Vetter(Goethe University Frankfurt), Boris Düdder(University of Copenhagen), Eleanore Hickman(University of Cambridge), Renee Wurth(Seoul National University), Frédérick Bruneault(Université du Québec à Montréal), Elisabeth Hildt(Illinois Institute of Technology), Sune Holm(University of Copenhagen), Jesmin Jahan Tithi(Intel (United States)), Roberto V. Zicari(Seoul National University), Julia Amann(ETH Zurich), Vince I. Madai(Berlin Institute of Health at Charité - Universitätsmedizin Berlin), Irmhild van Halem(Goethe University Frankfurt), Georgios Kararigas(University of Iceland), Pedro Kringen(Norwegian Cancer Society), Thilo Hagendorff(University of Stuttgart), Emilie Wiinblad Mathez, Alessio Gallucci(Eindhoven University of Technology), Thomas Krendl Gilbert(Cornell University), Megan Coffee, Magnus Westerlund(Arcada University of Applied Sciences)
Cited by 28
Related Papers
Explainability for artificial intelligence in healthcare: a multidisciplinary perspective
|BMC Medical Informatics and Decision Making|2020|1.8k
A U-Net Deep Learning Framework for High Performance Vessel Segmentation in Patients With Cerebrovascular Disease
|Frontiers in Neuroscience|2019|264
Non-Medical Use of Prescription Stimulants and Illicit Use of Stimulants for Cognitive Enhancement in Pupils and Students in Germany
|Pharmacopsychiatry|2010|250
Glutamatergic axons from the lateral habenula mainly terminate on GABAergic neurons of the ventral midbrain
|Neuroscience|2010|247
To explain or not to explain?—Artificial intelligence explainability in clinical decision support systems
|PLOS Digital Health|2022|205