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VERIFAI - A Step Towards Evaluating the Responsibility of AI-Systems

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.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.identifier.doi10.18420/BTW2023-63
dc.identifier.isbn978-3-88579-725-8
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/40372
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
gi.citation.endPage941
gi.citation.publisherPlaceBonn
gi.citation.startPage933
gi.conference.date06.-10. März 2023
gi.conference.locationDresden, Germany

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