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Designing Transparency for Effective Human-AI Collaboration

Author

Listed:
  • Michael Vössing

    (Karlsruhe Institute of Technology)

  • Niklas Kühl

    (Karlsruhe Institute of Technology)

  • Matteo Lind

    (Karlsruhe Institute of Technology)

  • Gerhard Satzger

    (Karlsruhe Institute of Technology)

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.

Suggested Citation

  • Michael Vössing & Niklas Kühl & Matteo Lind & Gerhard Satzger, 2022. "Designing Transparency for Effective Human-AI Collaboration," Information Systems Frontiers, Springer, vol. 24(3), pages 877-895, June.
    • Handle: RePEc:spr:infosf:v:24:y:2022:i:3:d:10.1007_s10796-022-10284-3
    DOI: 10.1007/s10796-022-10284-3
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    2. Xiaoyue Lin & Tiandong Wang & Fan Sheng, 2025. "Exploring the dual effect of trust in GAI on employees’ exploitative and exploratory innovation," Palgrave Communications, Palgrave Macmillan, vol. 12(1), pages 1-14, December.
    3. Daniel Leuthe & Tim Meyer-Hollatz & Tobias Plank & Anja Senkmüller, 2024. "Towards Sustainability of AI – Identifying Design Patterns for Sustainable Machine Learning Development," Information Systems Frontiers, Springer, vol. 26(6), pages 2103-2145, December.
    4. Simón, Cristina & Revilla, Elena & Jesús Sáenz, Maria, 2024. "Integrating AI in organizations for value creation through Human-AI teaming: A dynamic-capabilities approach," Journal of Business Research, Elsevier, vol. 182(C).
    5. Van Khanh Pham & Minh Le, 2024. "Impact of Artificial Intelligence on Environmental Quality through Technical Change: A Free Dynamic Equilibrium Approach," Papers 2410.06501, arXiv.org, revised Jun 2025.
    6. Babak Abedin & Christian Meske & Iris Junglas & Fethi Rabhi & Hamid R. Motahari-Nezhad, 2022. "Designing and Managing Human-AI Interactions," Information Systems Frontiers, Springer, vol. 24(3), pages 691-697, June.

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