IDEAS home Printed from https://ideas.repec.org/a/spr/infosf/v24y2022i3d10.1007_s10796-022-10284-3.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10796-022-10284-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10796-022-10284-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Armstrong, J. Scott, 2007. "Significance tests harm progress in forecasting," International Journal of Forecasting, Elsevier, vol. 23(2), pages 321-327.
    2. Jarrahi, Mohammad Hossein, 2018. "Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making," Business Horizons, Elsevier, vol. 61(4), pages 577-586.
    3. Alexander Maedche & Stefan Morana & Silvia Schacht & Dirk Werth & Julian Krumeich, 2016. "Advanced User Assistance Systems," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 58(5), pages 367-370, October.
    4. Sean J. Taylor & Benjamin Letham, 2018. "Forecasting at Scale," The American Statistician, Taylor & Francis Journals, vol. 72(1), pages 37-45, January.
    5. Berkeley J. Dietvorst & Joseph P. Simmons & Cade Massey, 2018. "Overcoming Algorithm Aversion: People Will Use Imperfect Algorithms If They Can (Even Slightly) Modify Them," Management Science, INFORMS, vol. 64(3), pages 1155-1170, March.
    6. Allen S. Lee & Richard L. Baskerville, 2003. "Generalizing Generalizability in Information Systems Research," Information Systems Research, INFORMS, vol. 14(3), pages 221-243, September.
    7. Unknown, 2016. "Proceedings Of Abstracts," 152nd Seminar, August 30 - September 1, 2016, Novi Sad, Serbia 244068, European Association of Agricultural Economists.
    8. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    9. Jan Brocke & Alexander Maedche, 2019. "The DSR grid: six core dimensions for effectively planning and communicating design science research projects," Electronic Markets, Springer;IIM University of St. Gallen, vol. 29(3), pages 379-385, September.
    10. Henning Kagermann, 2015. "Change Through Digitization—Value Creation in the Age of Industry 4.0," Springer Books, in: Horst Albach & Heribert Meffert & Andreas Pinkwart & Ralf Reichwald (ed.), Management of Permanent Change, edition 127, chapter 2, pages 23-45, Springer.
    11. Rob J. Hyndman, 2006. "Another Look at Forecast Accuracy Metrics for Intermittent Demand," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 4, pages 43-46, June.
    12. Collins, Christopher & Dennehy, Denis & Conboy, Kieran & Mikalef, Patrick, 2021. "Artificial intelligence in information systems research: A systematic literature review and research agenda," International Journal of Information Management, Elsevier, vol. 60(C).
    13. Robert Fildes & Paul Goodwin, 2007. "Good and Bad Judgment in Forecasting: Lessons from Four Companies," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 8, pages 5-10, Fall.
    14. Ujwal Kayande & Arnaud De Bruyn & Gary L. Lilien & Arvind Rangaswamy & Gerrit H. van Bruggen, 2009. "How Incorporating Feedback Mechanisms in a DSS Affects DSS Evaluations," Information Systems Research, INFORMS, vol. 20(4), pages 527-546, December.
    15. Peter Buxmann & Thomas Hess & Jason Bennett Thatcher, 2021. "AI-Based Information Systems," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 63(1), pages 1-4, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    2. Mahmud, Hasan & Islam, A.K.M. Najmul & Ahmed, Syed Ishtiaque & Smolander, Kari, 2022. "What influences algorithmic decision-making? A systematic literature review on algorithm aversion," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    3. Litsiou, Konstantia & Polychronakis, Yiannis & Karami, Azhdar & Nikolopoulos, Konstantinos, 2022. "Relative performance of judgmental methods for forecasting the success of megaprojects," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1185-1196.
    4. Franses, Philip Hans & Legerstee, Rianne, 2013. "Do statistical forecasting models for SKU-level data benefit from including past expert knowledge?," International Journal of Forecasting, Elsevier, vol. 29(1), pages 80-87.
    5. Kim, Sungil & Kim, Heeyoung, 2016. "A new metric of absolute percentage error for intermittent demand forecasts," International Journal of Forecasting, Elsevier, vol. 32(3), pages 669-679.
    6. Sroginis, Anna & Fildes, Robert & Kourentzes, Nikolaos, 2023. "Use of contextual and model-based information in adjusting promotional forecasts," European Journal of Operational Research, Elsevier, vol. 307(3), pages 1177-1191.
    7. Gaetano Perone, 2022. "Comparison of ARIMA, ETS, NNAR, TBATS and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 23(6), pages 917-940, August.
    8. Mahmud, Hasan & Islam, A.K.M. Najmul & Mitra, Ranjan Kumar, 2023. "What drives managers towards algorithm aversion and how to overcome it? Mitigating the impact of innovation resistance through technology readiness," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    9. Fildes, Robert & Wei, Yingqi & Ismail, Suzilah, 2011. "Evaluating the forecasting performance of econometric models of air passenger traffic flows using multiple error measures," International Journal of Forecasting, Elsevier, vol. 27(3), pages 902-922, July.
    10. Crone, Sven F. & Hibon, Michèle & Nikolopoulos, Konstantinos, 2011. "Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction," International Journal of Forecasting, Elsevier, vol. 27(3), pages 635-660, July.
    11. Alexander Maedche & Christine Legner & Alexander Benlian & Benedikt Berger & Henner Gimpel & Thomas Hess & Oliver Hinz & Stefan Morana & Matthias Söllner, 2019. "AI-Based Digital Assistants," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 61(4), pages 535-544, August.
    12. Yelland, Phillip M., 2010. "Bayesian forecasting of parts demand," International Journal of Forecasting, Elsevier, vol. 26(2), pages 374-396, April.
    13. Emilian Dobrescu, 2014. "Attempting to Quantify the Accuracy of Complex Macroeconomic Forecasts," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 5-21, December.
    14. Dominik Siemon, 2022. "Elaborating Team Roles for Artificial Intelligence-based Teammates in Human-AI Collaboration," Group Decision and Negotiation, Springer, vol. 31(5), pages 871-912, October.
    15. Jiao, Xiaoying & Chen, Jason Li & Li, Gang, 2021. "Forecasting tourism demand: Developing a general nesting spatiotemporal model," Annals of Tourism Research, Elsevier, vol. 90(C).
    16. Victor Richmond R. Jose, 2017. "Percentage and Relative Error Measures in Forecast Evaluation," Operations Research, INFORMS, vol. 65(1), pages 200-211, February.
    17. Vincent, Vinod U., 2021. "Integrating intuition and artificial intelligence in organizational decision-making," Business Horizons, Elsevier, vol. 64(4), pages 425-438.
    18. Hu, Qiwei & Boylan, John E. & Chen, Huijing & Labib, Ashraf, 2018. "OR in spare parts management: A review," European Journal of Operational Research, Elsevier, vol. 266(2), pages 395-414.
    19. Victor Richmond R. Jose, 2017. "Percentage and Relative Error Measures in Forecast Evaluation," Operations Research, INFORMS, vol. 65(1), pages 200-211, February.
    20. Posch, Konstantin & Truden, Christian & Hungerländer, Philipp & Pilz, Jürgen, 2022. "A Bayesian approach for predicting food and beverage sales in staff canteens and restaurants," International Journal of Forecasting, Elsevier, vol. 38(1), pages 321-338.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:infosf:v:24:y:2022:i:3:d:10.1007_s10796-022-10284-3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.