Z-Inspection®: A process to assess trustworthy AI
The Process
Z-Inspection®, to the best of our knowledge, is the first process to co-design and assess trustworthy AI in practice.
Our work is distributed under the terms and conditions of the Creative Commons (Attribution-NonCommercial-ShareAlike CC BY-NC-SA) license.
Download the Z-Inspection® Process
Roberto V. Zicari, John Brodersen, James Brusseau, Boris Düdder, Timo Eichhorn, Todor Ivanov, Georgios Kararigas , Pedro Kringen, Melissa McCullough, Florian Möslein, Karsten Tolle, Jesmin Jahan Tithi, Naveed Mushtaq, Gemma Roig , Norman Stürtz, Irmhild van Halem, Magnus Westerlund.
IEEE Transactions on Technology and Society, 2021
Print ISSN: 2637-6415
Online ISSN: 2637-6415
Digital Object Identifier: 10.1109/TTS.2021.3066209
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Education
We offer a comprehensive series of lectures on the Ethical implications of AI available online at no cost- open to all.
Co-design of Trustworthy AI. Best Practice.
Deep Learning based Skin Lesion Classifiers.
Jointly with the German Research Center for Artificial Intelligence GmbH (DFKI) we completed the first phase of the ethically aligned co-design methodology.
Assessing Trustworthy AI. Best Practice.
Machine learning as a supportive tool to recognize cardiac arrest in 112 emergency calls for the City of Copenhagen.
Jointly with the Emergency Medical Services Copenhagen (EMS), we completed the first part of our trustworthy AI assessment.
Kick off Meeting (April 15, 2021) Assessing Trustworthy AI. Best Practice: Deep Learning for predicting a multi-regional score conveying the degree of lung compromise in COVID-19 patients. In cooperation with Department of Information Engineering and Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health – University of Brescia, Brescia, Italy
/in General, News /by Roberto Zicari