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dc.contributor.authorGöllner, Sabrina
dc.contributor.authorTropmann-Frick, Marina
dc.contributor.editorKönig-Ries, Birgitta
dc.contributor.editorScherzinger, Stefanie
dc.contributor.editorLehner, Wolfgang
dc.contributor.editorVossen, Gottfried
dc.date.accessioned2023-02-23T14:00:13Z
dc.date.available2023-02-23T14:00:13Z
dc.date.issued2023
dc.identifier.isbn978-3-88579-725-8
dc.identifier.urihttp://dl.gi.de/handle/20.500.12116/40372
dc.description.abstractThis work represents the first step towards a unified framework for evaluating an AI system's responsibility by building a prototype application.The python based web-application uses several libraries for testing the fairness, robustness, privacy, and explainability of a machine-learning model as well as the dataset which was used for training the model.The workflow of the prototype is tested and described using images of a healthcare dataset since healthcare represents an area where automatic decisions affect decisions about human lives, and building responsible AI in this area is therefore indispensable.en
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofBTW 2023
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-331
dc.subjectArtificial Intelligence
dc.subjectResponsible AI
dc.subjectPrivacy-preserving AI
dc.subjectExplainable AI
dc.subjectEthical AI
dc.subjectTrustworthy AI
dc.titleVERIFAI - A Step Towards Evaluating the Responsibility of AI-Systemsen
dc.typeText/Conference Paper
dc.identifier.doi10.18420/BTW2023-63
gi.conference.locationDresden, Germany
gi.conference.date06.-10. März 2023
gi.citation.startPage933
gi.citation.endPage941
gi.citation.publisherPlaceBonn


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