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Designing Transparency for Effective Human-AI Collaboration

Vössing, Michael, Kühl, Niklas, Lind, Matteo, Satzger, Gerhard·2022·Information Systems Frontiers
DOI 10.1007/s10796-022-10284-3
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Abstract

The field of artificial intelligence (AI) is advancing quickly, and systems can increasingly perform a multitude of tasks that previously required human intelligence. Information systems can facilitate collaboration between humans and AI systems such that their individual capabilities complement each other. However, there is a lack of consolidated design guidelines for information systems facilitating the collaboration between humans and AI systems. This work examines how agent transparency affects trust and task outcomes in the context of human-AI collaboration. Drawing on the 3-Gap framework, we study agent transparency as a means to reduce the information asymmetry between humans and the AI. Following the Design Science Research paradigm, we formulate testable propositions, derive design requirements, and synthesize design principles. We instantiate two design principles as design features of an information system utilized in the hospitality industry. Further, we conduct two case studies to evaluate the effects of agent transparency: We find that trust increases when the AI system provides information on its reasoning, while trust decreases when the AI system provides information on sources of uncertainty. Additionally, we observe that agent transparency improves task outcomes as it enhances the accuracy of judgemental forecast adjustments.

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Metadata

Authors
Vössing, M., Kühl, N., Lind, M., & Satzger, G.
ORCID
Vössing, Michael
Venue
Information Systems Frontiers
Publisher
Springer Science and Business Media LLC
Published
2022-05-26
Volume
24
Issue
3
Pages
877-895
ISSN
  • 1387-3326
  • 1572-9419
DOI
10.1007/s10796-022-10284-3
Cited by
223
References
23
Language
en
Licence
Funders
Karlsruher Institut für Technologie (KIT)

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