Abstract
This study examines the effects of AI transparency in AI-human task delegation. According to the principal-agent theory (PAT), increasing transparency can help address the problem of hidden action. Through a between-subjects experiment with three conditions (AI advantage information, AI function information, vs. no information), we explore the impact of AI transparency on human task performance and consider the mediating effect of human epistemic uncertainty and AI trust. Results show that AI function information enhances accuracy, while AI advantage information encourages more task completion. Epistemic uncertainty fully mediates the relationship between transparency and task performance (both accuracy and image number), while AI trust only mediates the effect on image number. The results have implications for the future work of the design of AI transparency in human-AI collaboration.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Jiang, J., Karran, A.J., Coursaris, C.K., Léger, P.M., Beringer, J.: A situation awareness perspective on human-AI interaction: tensions and opportunities. Int. J. Hum. Comput. Interact. 39(9), 1789–1806 (2022). https://doi.org/10.1080/10447318.2022.2093863
Fügener, A., Grahl, J., Gupta, A., et al.: Cognitive challenges in human-artificial intelligence collaboration: investigating the path toward productive delegation. Inf. Syst. Res. 33(2), 678–696 (2022)
Baird, A., Maruping, L.M.: The next generation of research on IS use: a theoretical framework of delegation to and from agentic IS artifacts. MIS Q. 45(1), 315–341 (2021). https://doi.org/10.25300/MISQ/2021/15882
Candrian, C., Scherer, A.: Rise of the machines: delegating decisions to autonomous AI. Comput. Hum. Behav. 134, 107308 (2022)
Hemmer, P., Westphal, M., Schemmer, M., et al.: Human-AI collaboration: the effect of AI delegation on human task performance and task satisfaction. In: Proceedings of the 28th International Conference on Intelligent User Interfaces (2023)
Afzal, S., Arshad, M., Saleem, S., et al.: The impact of perceived supervisor support on employees’ turnover intention and task performance: mediation of self-efficacy. J. Manage. Dev. 38(5), 369–382 (2019)
Wang, X., Yin, M.: Are explanations helpful? a comparative study of the effects of explanations in AI-assisted decision-making. In: 26th International Conference on Intelligent User Interfaces (2021)
Chen, J.Y., Procci, K., Boyce, M., Wright, J., Garcia, A., Barnes, M.: Situation awareness-based agent transparency (No. ARL-TR-6905). Aberdeen Proving Ground, MD: U.S. Army Res. Lab. 1–29 (2014)
Mercado, J.E., Rupp, M.A., Chen, J.Y., et al.: Intelligent agent transparency in human–agent teaming for multi-UxV management. Hum. Factors 58(3), 401–415 (2016)
Liu, B.: In AI we trust? Effects of agency locus and transparency on uncertainty reduction in human–AI interaction. J. Comput.-Mediat. Commun. 26(6), 384–402 (2021)
Stowers, K., Kasdaglis, N., Newton, O., et al.: 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 Annual Meeting. SAGE Publications Sage CA: Los Angeles, CA (2016)
Fayezi, S., O’Loughlin, A., Zutshi, A.: Agency theory and supply chain management: a structured literature review. Supply Chain Manage. Int. J. 17(5), 556–570 (2012)
Guggenberger, T., Lämmermann, L., Urbach, N., Walter, A.M., Hofmann, P.: Task delegation from AI to humans: a principal-agent perspective. In: ICIS (2023)
Conway, J.M.: Distinguishing contextual performance from task performance for managerial jobs. J. Appl. Psychol. 84(1), 3 (1999)
Charles, R., Berger, J.J.B.: Language and social knowledge: Uncertainty in interpersonal relations. Lond Edward Arnold. Lang. Soc. 13(1), 87–90 (1984)
Mayer, R.C., Davis, J.H., Schoorman, F.D.: An integrative model of organizational trust. Acad. Manag. Rev. 20(3), 709–734 (1995)
Kizilcec, R.F.: How much information? Effects of transparency on trust in an algorithmic interface. In: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (2016)
Zhang, Y., Liao, Q.V., Bellamy, R.K.: Effect of confidence and explanation on accuracy and trust calibration in AI-assisted decision making. In: Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (2020)
Perrig, S.A., Scharowski, N., Brühlmann, F.: Trust issues with trust scales: examining the psychometric quality of trust measures in the context of AI. In: Extended abstracts of the 2023 CHI Conference on Human Factors in Computing Systems (2023)
Ferris, K.R.: Perceived uncertainty and job satisfaction in the accounting environment. Acc. Organ. Soc. 2(1), 23–28 (1977)
Schepman, A., Rodway, P.: The general attitudes towards artificial intelligence scale (GAAIS): confirmatory validation and associations with personality, corporate distrust, and general trust. Int. J. Hum. Comput. Interact. 39(13), 2724–2741 (2023)
Hayes, A.F.: Introduction to mediation, moderation, and conditional process analysis: A Regression-Based Approach. Guilford Publications (2017)
Bhatt, U., et al.: Uncertainty as a form of transparency: measuring, communicating, and using uncertainty. In: Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society, (2021)
Acknowledgement
This work was partially supported by the Social Science Fund Research Base Project of Beijing (19JDGLB029).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Wang, Y., Jiang, Y., Tang, J., Zhou, X. (2024). Task Delegation from AI to Humans: The Impact of AI Transparency on Human Performance. In: Tu, Y.P., Chi, M. (eds) E-Business. New Challenges and Opportunities for Digital-Enabled Intelligent Future. WHICEB 2024. Lecture Notes in Business Information Processing, vol 517. Springer, Cham. https://doi.org/10.1007/978-3-031-60324-2_24
Download citation
DOI: https://doi.org/10.1007/978-3-031-60324-2_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-60326-6
Online ISBN: 978-3-031-60324-2
eBook Packages: Computer ScienceComputer Science (R0)