The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
Designing Transparency for Effective Human-AI Collaboration
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Meanwhile, the support information at the user's discretion was identified in eight cases (Calisto et al., 2022;Vössing et al., 2022;Suresh et al., 2020;van der Waa et al., 2021;Liu et al., 2021;Molina and Sundar, 2022;Prabhudesai et al., 2023), for demanding explanations in particular.”
From Human-AI collaboration is not very collaborative yet: a taxonomy of interaction patterns in AI-assisted decision making from a systematic review (2025) · cites this paper as Vössing et al., 2022
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A Unified Approach to Interpreting Model Predictions
Lundberg, S., & Lee, S.-I. (2017) · arXiv · doi:10.48550/arxiv.1705.07874 · Cited by 23,707
Palinkas, L. A., Horwitz, S. M., Green, C. A., Wisdom, J. P., Duan, N., & Hoagwood, K. (2015) · Administration and Policy in Mental Health and Mental Health Services Research · doi:10.1007/s10488-013-0528-y · Cited by 12,038
Design Science in Information Systems Research1
Hevner, A. R., March, S. T., Park, J., & Ram, S. (2004) · MIS Quarterly · doi:10.2307/25148625 · Cited by 10,707 · cited 2×
“We conducted two case studies to demonstrate the utility of the developed artifacts in a naturalistic setting (Hevner et al., 2004).”
MethodAffect and Cognition Based Trust as Foundations for Interpersonal Cooperation in Organizations
McAllister, D. J. (1995) · Academy of Management Journal · doi:10.2307/256727 · Cited by 7,346
Trust in Automation: Designing for Appropriate Reliance
Lee, J. D., & See, K. A. (2004) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1518/hfes.46.1.50_30392 · Cited by 6,605
Another look at measures of forecast accuracy
Hyndman, R. J., & Koehler, A. B. (2006) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2006.03.001 · Cited by 5,757
Humans and automation: Use, misuse, disuse, abuse
Parasuraman, R., & Riley, V. (1997) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1518/001872097778543886 · Cited by 4,554 · cited 2×
“This behavior also indicates an under-reliance on the AI (Parasuraman & Riley, 1997).”
BackgroundExplaining Explanations: An Overview of Interpretability of Machine Learning
Gilpin, L. H., Bau, D., Yuan, B. Z., Bajwa, A., Specter, M., & Kagal, L. (2018) · 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa.2018.00018 · Cited by 2,507
Ardila, D., Kiraly, A. P., Bharadwaj, S., Choi, B., Reicher, J. J., Peng, L., Tse, D., Etemadi, M., Ye, W., Corrado, G., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0447-x · Cited by 2,167
Dietvorst, B. J., Simmons, J. P., & Massey, C. (2018) · Management Science · doi:10.1287/mnsc.2016.2643 · Cited by 1,230 · cited 2×
“As noted by Dietvorst et al. (2018), the “reluctance to use algorithms for forecasting [often] stems from an intolerance of inevitable error” (p. 1156).”
BackgroundResult
Research works printed but not linked: 79
- Abdul, A., Vermeulen, J., Wang, D., Lim, B.Y., & Kankanhalli, M. (2018). Trends and trajectories for explainable, accountable and intelligible systems: An HCI research agenda. In Proceedings of the 2018 CHI conference on human factors in computing systems (pp. 1–18). · DOI · Paper page
- Armstrong, J. (2007). Scott Significance tests harm progress in forecasting. International Journal of Forecasting, 23(2), 321–327. · DOI
- Avital, M., Hevner, A., & Schwartz, D (2019). It takes two to Tango: Choreographing the interactions between human and artificial intelligence. In Proceedings of the 27th european conference on information systems (ECIS). Stockholm & Uppsala, Sweden.
- Benyon, D. (2014). Designing Interactive Systems: A comprehensive guide to HCI, UX and interaction design. United Kingdom: Pearson, 3rd edition.
- Berente, N., Gu, B., Recker, J., & Santhanam, R. (2021). Managing artificial intelligence. Management Information Systems Quarterly, 45(3).
- Bertsimas, D., Dunn, J., & Mundru, N. (2019). Optimal prescriptive trees. INFORMS Journal on Optimization, 1(2), 164–183. · DOI
- Blattberg, RC., & Hoch, SJ. (2008). Database models and managerial intuition: 50% Model + 50Manager. Management Science, 36(8), 887–899. · DOI · Paper page
- Buxmann, P., Hess, T., & Thatcher, J.B. (2021). AI-based information systems. Business & Information Systems Engineering, 63(1), 1–4. · DOI
- Castelfranchi, C., & Falcone, R. (2000). Trust and control: A dialectic link. Applied Artificial Intelligence, 14(8), 799–823. · DOI
- Chen, J.Y.C., Procci, K., Boyce, M., Wright, J., Garcia, A., & Barnes, M.J. (2014). Situation awareness–based agent transparency. US Army Research Laboratory, pp. 1–29. · DOI · Paper page
- Crouser, R.J., & Chang, R. (2012). An affordance-based framework for human computation and human-computer collaboration. IEEE Transactions on Visualization and Computer Graphics, 18(12), 2859–2868. · DOI
- Dellermann, D., Ebel, P., Söllner, M., & Leimeister, J.M. (2019a). Hybrid intelligence. Business & Information Systems Engineering, 61(5), 637–643. · DOI · Paper page
- Dellermann, D., Lipusch, N., Ebel, P., & Leimeister, J.M. (2019b). Design principles for a hybrid intelligence decision support system for business model validation. Electronic Markets, 29(3), 423–441. · DOI
- Drury, J.L., Scholtz, J., & Yanco, H.A. (2003). Awareness in human-robot interactions. In Proceedings of the 2003 IEEE international conference on systems, man and cybernetics (pp. 912–918). IEEE. · DOI · Paper page
- Einhorn, H.J. (1974). Expert judgment: Some necessary conditions and an example. Journal of Applied Psychology, 59(5), 562. · DOI · Paper page
- Endsley, M.R. (1995). Toward a theory of situation awareness in dynamic systems. Human Factors, 37(1), 32–64. · DOI · Paper page
- Fildes, R., & Goodwin, P. (2013). Forecasting support systems: What we know, what we need to know. International Journal of Forecasting, 29(2), 290–294. · DOI
- Fildes, R., Goodwin, P., & et al. (2007). Good and bad judgement in forecasting: Lessons from four companies. Foresight, 8, 5–10.
- Fleischmann, K.R., & Wallace, W.A. (2005). A covenant with transparency: Opening the black box of models. Communications of the ACM, 48(5), 93–97. · DOI
- Giboney, J.S., Brown, SA., Lowry, P.B., & Nunamaker, J.F. (2015a). User acceptance of knowledge-based system recommendations: Explanations, arguments, and fit. Decision Support Systems, 72, 1–10. · DOI
- Gomm, R., Hammersley, M., & Foster, P. (2000). Case study and generalization. In R. Gomm, M. Hammersley, & P Foster (Eds.) Case study method (pp. 98–115). · DOI
- Gregor, S., & Hevner, A.R. (2013). Positioning and presenting design science research for maximum impact. MIS Quarterly, 37(2), 337–355. · DOI · Paper page
- Wilson, H.J., Daugherty, P.R., Wilson, J., & Daugherty, P. (2018). Collaborative intelligence: Humans and AI are joining forces. Harvard Business Review, 96(4), 114–123.
- Harper, R.H.R. (2019). The role of HCI in the Age of AI. International Journal of Human–Computer Interaction, 35(15), 1331–1344. · DOI · Paper page
- Patrick, H., Schemmer, M., Vössing, M., & Kühl, N. (2021). Human-AI complementarity in hybrid intelligence systems: A structured literature review. In PACIS 2021 Proceedings.
- Hengstler, M., Enkel, E., & Duelli, S. (2016). Applied artificial intelligence and trust—The case of autonomous vehicles and medical assistance devices. Technological Forecasting and Social Change, 105, 105–120. · DOI · Paper page
- Holzinger, A. (2016). Interactive machine learning for health informatics: when do we need the human-in-the-loop? Brain Informatics, 3(2), 119–131. · DOI · Paper page
- Hosseini, M., Shahri, A., Phalp, K., & Ali, R. (2017). Engineering transparency requirements: A modelling and analysis framework. Information Systems, 74(1), 3–22.
- Hosseini, M., Shahri, A., Phalp, K., & Ali, R. (2018). Four reference models for transparency requirements in information systems. Requirements Engineering, 23(2), 251–275. · DOI
- Hyndman, R.J. (2006). Another look at forecast-accuracy metrics for intermittent demand. Foresight: The International Journal of Applied Forecasting, 4(4), 43–46.
- Jarke, M., Loucopoulos, P., Lyytinen, K., Mylopoulos, J., & Robinson, W. (2011). The brave new world of design requirements. Information Systems, 36(7), 992–1008. · DOI
- Jarrahi, M.H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons, 61(4), 577–586. · DOI · Paper page
- Jensen, ML., Lowry, P.B., Burgoon, JK., & Nunamaker, J.F. (2010). Technology dominance in complex decision making: The case of aided credibility assessment. Journal of Management Information Systems, 27(1), 175–202. · DOI
- Jones, D., & Gregor, S. (2007). The anatomy of a design theory. Journal of the Association for Information Systems, 8(5), 1. · DOI · Paper page
- Kamar, E. (2016). Directions in hybrid intelligence: Complementing AI systems with human intelligence. In IJCAI international joint conference on artificial intelligence (pp. 4070–4073).
- Kayande, U., De Bruyn, A., Lilien, G.L., Rangaswamy, A., & Van Bruggen, G.H. (2009). How incorporating feedback mechanisms in a DSS affects DSS evaluations. Information Systems Research, 20(4), 527–546. · DOI
- Ke, G., Qi, M., Finley, Tx, Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T.-Y. (2017). Lightgbm: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems, 30, 3146–3154.
- Der Kiureghian, A., & Ditlevsen, O. (2009). Aleatory or epistemic? Does it matter? Structural Safety, 31(2), 105–112. · DOI · Paper page
- Kurzweil, R. (1990). The age of intelligent machines. MIT Press.
- Lee, A.S., & Baskerville, R.L. (2003). Generalizing generalizability in information systems research. Information Systems Research, 14(3), 221–243. · DOI · Paper page
- Leite, J.C.S.D.P., & Cappelli, C. (2010). Software transparency. Business & Information Systems Engineering, 2(3), 127–139. · DOI
- Lim, J.S, & O’Connor, M. (1995). Judgemental adjustment of initial forecasts: Its effectiveness and biases. Journal of Behavioral Decision Making, 8(3), 149–168. · DOI · Paper page
- Madsen, M., & Gregor, S. (2000). Measuring human-computer trust. Proceedings of 11th Australasian Conference on Information Systems, 53, 6–8.
- Maedche, A., Morana, S., Schacht, S., Werth, D., & Krumeich, J. (2016). Advanced user assistance systems. Business & Information Systems Engineering, 58(5), 367–370. · DOI
- Mathews, BP., & Diamantopoulous, A. (1990). Judgemental revision of sales forecasts: Effectiveness of forecast selection. Journal of Forecasting, 9(4), 407–415. · DOI
- Christian, M., & Bunde, E. (2020). Transparency and trust in Human-AI-interaction: The role of model-agnostic explanations in computer vision-based decision support. In D. Helmut L. Reinerman-Jones (Eds.) Artificial intelligence in HCI (pp. 54–69). Cham: Springer International Publishing. · DOI
- Meske, C., Bunde, E., Schneider, J., & Gersch, M. (2020). Explainable artificial intelligence: Objectives, stakeholders, and future research opportunities. Information Systems Management, pp 1–11. · DOI · Paper page
- Morana, S., Pfeiffer, J., & Adam, M.T.P. (2018). Call for papers, Issue 3/2020, user assistance for intelligent systems. Business & Information Systems Engineering, 60(6), 571–572. · DOI
- Mothilal, R.K., Sharma, A., & Tan, C. (2020). Explaining machine learning classifiers through diverse counterfactual explanations. In Proceedings of the 2020 conference on fairness, accountability, and transparency (pp. 607–617). · DOI · Paper page
- Muir, B.M. (1987). Trust between humans and machines. International Journal of Man-Machine Studies, 27, 327–339. · DOI · Paper page
- Ford, N.F. (1985). Decision support systems and expert systems: A comparison. Information & Management, 8(1), 21–26. · DOI
- Nunes, I., & Jannach, D. (2017). A systematic review and taxonomy of explanations in decision support and recommender systems. User Modeling and User-Adapted Interaction, 27(3-5), 393–444. · DOI · Paper page
- O’Donovan, J., & Smyth, B. (2005). Trust in recommender systems. In Proceedings of the 10th international conference on Intelligent user interfaces (pp. 167–174). ACM. · DOI · Paper page
- Patton, M.Q. (2002). Qualitative research and evaluation methods, vol. 3rd. Sage Publications.
- Petropoulos, F., Fildes, R., & Goodwin, P. (2016). Do ‘big losses’ in judgmental adjustments to statistical forecasts affect experts’ behaviour? European Journal of Operational Research, 249(3), 842–852. · DOI · Paper page
- Phillips-Wren, G. (2013). Intelligent decision support systems. Multicriteria Decision Aid and Artificial Intelligence: Links, Theory and Applications, pp. 25–43. · DOI
- Power, D.J., Heavin, C., & Keenan, P. (2019). Decision systems redux. Journal of Decision Systems, 28(1), 1–18. · DOI
- Rai, A., Constantinides, P., & Sarker, S. (2019). Next-generation digital platforms: Toward Human–AI hybrids. MIS Quarterly, 43(1), iii–x.
- Russell, S., & Norvig, P. (2016). Artificial intelligence: A modern approach. Pearson Education Limited, pp. 1151.
- Seeber, I., Bittner, E., Briggs, RO., de Vreede, T., de Vreede, G.-J., Elkins, A., Maier, R., Merz, A., Oeste-Reiß, S., Randrup, N., Schwabe, G., & Söllner, M. (2019). Machines as teammates: A research agenda on AI in team collaboration.Information & Management. · DOI
- Siau, K., & Wang, W. (2018). Building trust in artificial intelligence, machine learning, and robotics. Cutter Business Technology Journal, 31(2).
- Söllner, M, Benbasat, I., Gefen, D., Leimeister, J.M., & Pavlou, PA. (2016a). Trust. In A. Bush A. Rai (Eds.) MIS quarterly research curations.
- Söllner, M, Hoffmann, A., Hoffmann, H., Wacker, A., & Leimeister, J.M. (2012). Understanding the formation of trust in IT artifacts. In J.F. George (Ed.) Proceedings of the international conference on information systems ICIS 2012.
- Sonnenberg, C., & Brocke, J.V. (2012). Evaluations in the science of the artificial – reconsidering the build-evaluate pattern in design science research. In K. Pfeffers, M.A. Rothenberger, & B. Kuechler (Eds.) Design science research in information systems. Advances in theory and practice. DESRIST 2012. Lecture Notes in Computer Science (pp. 381–397). Springer Berlin Heidelberg. · DOI · Paper page
- Stowers, K., Kasdaglis, N., Newton, O., Lakhmani, S., Wohleber, R., & Chen, J. (2016). Intelligent agent transparency: The design and evaluation of an interface to facilitate human and intelligent agent collaboration. In Proceedings of the human factors and ergonomics society (pp. 1704–1708). · DOI
- Street, C.T., & Meister, D.B. (2004). Small business growth and internal transparency: The role of information systems. MIS Quarterly, pp 473–506. · DOI
- Taylor, S.J., & Letham, B. (2018). Forecasting at scale. The American Statistician, 72(1), 37–45. · DOI · Paper page
- Terveen, L.G. (1995). Overview of human-computer collaboration. Knowledge-Based Systems, 8 (2-3), 67–81. · DOI
- Tremblay, M.C., Hevner, A.R., & Berndt, D.J. (2010). The use of focus groups in design science research. In H. Alan S. Chatterjee (Eds.) Design research in information systems (pp. 121–143). Boston, MA: Springer. · DOI
- Tsang, E.W.K. (2014). Generalizing from research findings: The merits of case studies. International Journal of Management Reviews, 16(4), 369–383. · DOI · Paper page
- Tschannen-Moran, M. (2001). Collaboration and the need for trust. Journal of Educational Administration, 39(4), 308–331. · DOI · Paper page
- Venable, J., Pries-Heje, J., & Baskerville, R. (2016). FEDS: A framework for evaluation in design science research. European Journal of Information Systems, 25(1), 77–89. · DOI · Paper page
- vom Brocke, J., & Maedche, A. (2019). The DSR grid: six core dimensions for effectively planning and communicating design science research projects. Electronic Markets, 29(3), 379–385. · DOI
- Vössing, M., Potthoff, F., Kühl, N., & Satzger, G. (2019). Designing useful transparency to improve process performance—evidence from an automated production line. In Proceedings of the 27th european conference on information systems (ECIS).
- Wang, N., Pynadath, D.V., & Hill, S.G. (2016a). Trust calibration within a human-robot team: Comparing automatically generated explanations. In The Eleventh ACM/IEEE international conference on human robot interaction (pp. 109–116). IEEE Press. · DOI · Paper page
- Wang, W., & Benbasat, I. (2005). Trust in and adoption of online recommendation agents. Journal of the Association for Information Systems, 6(3), 72–101. · DOI · Paper page
- Wang, W., Qiu, L., Kim, D., & Benbasat, I. (2016b). Effects of rational and social appeals of online recommendation agents on cognition- and affect-based trust. Decision Support Systems, 86, 48–60. · DOI · Paper page
- Watson, H.J. (2017). Preparing for the cognitive generation of decision support. MIS Quarterly Executive, 16(3).
- Zschech, P., Walk, J., Heinrich, K., Vössing, M., & Kühl, N. (2021). A picture is worth a collaboration: Accumulating design knowledge for computer-vision-based hybrid intelligence systems. In 29th european conference on information systems (ECIS 2021), June 14-16, 2021.
Cited by
Gomez, C., Cho, S. M., Ke, S., Huang, C.-M., & Unberath, M. (2025) · Frontiers in Computer Science · doi:10.3389/fcomp.2024.1521066 · Cited by 209
Schöbel, S., Schmitt, A., Benner, D., Saqr, M., Janson, A., & Leimeister, J. M. (2024) · Information Systems Frontiers · doi:10.1007/s10796-023-10375-9 · Cited by 127
Agentic AI: A Comprehensive Survey of Technologies, Applications, and Societal Implications
Pati, A. K. (2025) · IEEE Access · doi:10.1109/access.2025.3585609 · Cited by 100
A multifaceted vision of the human-AI collaboration: a comprehensive review
Puerta-Beldarrain, M., Gómez-Carmona, O., Sánchez-Corcuera, R., Casado-Mansilla, D., López-de-Ipiña, D., & Chen, L. (2025) · IEEE Access · doi:10.1109/access.2025.3536095 · Cited by 73
AI Knowledge: Improving AI Delegation through Human Enablement
Pinski, M., Adam, M., & Benlian, A. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3580794 · Cited by 62
Metadata
- Authors
- Vössing, M., Kühl, N., Lind, M., & Satzger, G.
- ORCID
- Vössing, Michael
- Venue
- Information Systems Frontiers
- Published
- 2022-05-26
- Volume
- 24
- Issue
- 3
- Pages
- 877-895
- ISSN
- 1387-3326
- 1572-9419
- Cited by
- 223
- References
- 23
- Language
- en
- Licence
- creativecommons.org/licenses/by/4.0 (tdm)
- creativecommons.org/licenses/by/4.0 (vor)
- Funders
- Karlsruher Institut für Technologie (KIT)
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Designing Transparency for Effective Human-AI Collaboration. https://app.cochat.ai/papers/10.1007/s10796-022-10284-3
- The paper
- Vössing, M., Kühl, N., Lind, M., & Satzger, G. (2022). Designing Transparency for Effective Human-AI Collaboration. Information Systems Frontiers. https://doi.org/10.1007/s10796-022-10284-3
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.