Show simple item record

dc.contributor.authorAnuyah, Oghenemaro
dc.contributor.authorFine, William
dc.contributor.authorMetoyer, Ronald
dc.contributor.editorWienrich, Carolin
dc.contributor.editorWintersberger, Philipp
dc.contributor.editorWeyers, Benjamin
dc.date.accessioned2021-09-05T18:56:36Z
dc.date.available2021-09-05T18:56:36Z
dc.date.issued2021
dc.identifier.urihttp://dl.gi.de/handle/20.500.12116/37372
dc.description.abstractExplanations can help users of Artificial Intelligent (AI) systems gain a better understanding of the reasoning behind the model’s decision, facilitate their trust in AI, and assist them in making informed decisions. Due to its numerous benefits in improving how users interact and collaborate with AI, this has stirred the AI/ML community towards developing understandable or interpretable models to a larger degree, while design researchers continue to study and research ways to present explanations of these models’ decisions in a coherent form. However, there is still the lack of intentional design effort from the HCI community around these explanation system designs. In this paper, we contribute a framework to support the design and validation of explainable AI systems; one that requires carefully thinking through design decisions at several important decision points. This framework captures key aspects of explanations ranging from target users, to the data, to the AI models in use. We also discuss how we applied our framework to design an explanation interface for trace link prediction of software artifacts.en
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofMensch und Computer 2021 - Workshopband
dc.relation.ispartofseriesMensch und Computer
dc.subjectExplainable AI
dc.subjectinteraction design
dc.subjectdesign activity
dc.subjectartificial intelligence
dc.subjectmachine learning
dc.subjectuser research
dc.subjectdesign decision
dc.titleDesign Decision Framework for AI Explanationsen
dc.typeText/Conference Poster
dc.pubPlaceBonn
mci.document.qualitydigidoc
mci.conference.sessiontitleMCI-WS02: UCAI 2021: Workshop on User-Centered Artificial Intelligence
mci.conference.locationIngolstadt
mci.conference.date5.-8. September 2021
dc.identifier.doi10.18420/muc2021-mci-ws02-237


Files in this item

Thumbnail

Show simple item record