VERIFAI - A Step Towards Evaluating the Responsibility of AI-Systems
dc.contributor.author | Göllner, Sabrina | |
dc.contributor.author | Tropmann-Frick, Marina | |
dc.contributor.editor | König-Ries, Birgitta | |
dc.contributor.editor | Scherzinger, Stefanie | |
dc.contributor.editor | Lehner, Wolfgang | |
dc.contributor.editor | Vossen, Gottfried | |
dc.date.accessioned | 2023-02-23T14:00:13Z | |
dc.date.available | 2023-02-23T14:00:13Z | |
dc.date.issued | 2023 | |
dc.description.abstract | This 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.doi | 10.18420/BTW2023-63 | |
dc.identifier.isbn | 978-3-88579-725-8 | |
dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/40372 | |
dc.language.iso | en | |
dc.publisher | Gesellschaft für Informatik e.V. | |
dc.relation.ispartof | BTW 2023 | |
dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) - Proceedings, Volume P-331 | |
dc.subject | Artificial Intelligence | |
dc.subject | Responsible AI | |
dc.subject | Privacy-preserving AI | |
dc.subject | Explainable AI | |
dc.subject | Ethical AI | |
dc.subject | Trustworthy AI | |
dc.title | VERIFAI - A Step Towards Evaluating the Responsibility of AI-Systems | en |
dc.type | Text/Conference Paper | |
gi.citation.endPage | 941 | |
gi.citation.publisherPlace | Bonn | |
gi.citation.startPage | 933 | |
gi.conference.date | 06.-10. März 2023 | |
gi.conference.location | Dresden, Germany |
Dateien
Originalbündel
1 - 1 von 1