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Trustworthy Artificial Intelligence

Becker, Maximilian ORCID iD icon

Abstract (englisch):

Trust or trustworthiness are hard to define. There are many aspects that can increase or decrease the trust in an Artificial Intelligence systems. This is why entities such as the High-level expert group on AI (HLEG) and the European commission’s artificial intelligence act are putting forward guidelines and regulations demand trustworthiness and help to better define it. One aspect that can increase the trust in a system is to make the system more transparent. For AI systems this can be achieved through Explainable AI or XAI which has the goal to explain learning systems. This article will list some requirements from the HLEG and the European artificial intelligence act and will go further into transparency and how it can be achieved through explanations. At the end we will cover personalized explanations, how they could be achieved and how they could benefit users.


Verlagsausgabe §
DOI: 10.5445/IR/1000148316
Veröffentlicht am 11.07.2022
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Anthropomatik und Robotik (IAR)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2022
Sprache Englisch
Identifikator ISBN: 978-3-7315-1171-7
ISSN: 1863-6489
KITopen-ID: 1000148316
HGF-Programm 46.23.04 (POF IV, LK 01) Engineering Security for Production Systems
Erschienen in Proceedings of the 2021 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory
Veranstaltung Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory (2021), Karlsruhe, Deutschland, 02.07.2021 – 06.07.2021
Verlag Karlsruher Institut für Technologie (KIT)
Seiten 21-32
Serie Karlsruher Schriften zur Anthropomatik / Lehrstuhl für Interaktive Echtzeitsysteme, Karlsruher Institut für Technologie ; Fraunhofer-Inst. für Optronik, Systemtechnik und Bildauswertung IOSB Karlsruhe ; 54
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