Göllner, SabrinaTropmann-Frick, MarinaKönig-Ries, BirgittaScherzinger, StefanieLehner, WolfgangVossen, Gottfried2023-02-232023-02-232023978-3-88579-725-8https://dl.gi.de/handle/20.500.12116/40372This 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.enArtificial IntelligenceResponsible AIPrivacy-preserving AIExplainable AIEthical AITrustworthy AIText/Conference Paper10.18420/BTW2023-63