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dc.contributor.authorScheerer, Max
dc.date.accessioned2023-10-31T13:53:19Z
dc.date.available2023-10-31T13:53:19Z
dc.date.issued2023
dc.identifierOCN: 1410495349
dc.identifier.urihttps://library.oapen.org/handle/20.500.12657/77095
dc.description.abstractAlthough tremendous progress has been made in Artificial Intelligence (AI), it entails new challenges. The growing complexity of learning tasks requires more complex AI components, which increasingly exhibit unreliable behaviour. In this book, we present a model-driven approach to model architectural safeguards for AI components and analyse their effect on the overall system reliability.en_US
dc.languageEnglishen_US
dc.relation.ispartofseriesThe Karlsruhe Series on Software Design and Qualityen_US
dc.subject.classificationbic Book Industry Communication::P Mathematics & scienceen_US
dc.subject.otherself-adaptive systems; safeguarding AI; architectural reliability analysis; Software engineering; Selbst-Adaptive Systeme; Absicherung von KI; architekturelle Zuverlässigkeitsanalyse; Softwaretechniken_US
dc.titleEvaluating Architectural Safeguards for Uncertain AI Black-Box Componentsen_US
dc.typebook
oapen.identifier.doi10.5445/KSP/1000161585en_US
oapen.relation.isPublishedBy44e29711-8d53-496b-85cc-3d10c9469be9en_US
oapen.collectionAG Universitätsverlage
oapen.series.number39en_US
oapen.pages472en_US
peerreview.anonymityAll identities known
peerreview.id51a542ec-eaeb-47c2-861d-6022e981a97a
peerreview.open.reviewNo
peerreview.publish.responsibilityBooks or series editor
peerreview.review.stagePre-publication
peerreview.review.typeFull text
peerreview.reviewer.typeEditorial board member
peerreview.reviewer.typeExternal peer reviewer
peerreview.titleDissertations in Series (Dissertationen in Schriftenreihe)


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