Cited by 18Human-Automation Interaction and SafetySocial Robot Interaction and HRIUsability and User Interface Design

Human Delegation Behavior in Human-AI Collaboration: The Effect of Contextual Information

Open access
Spitzer, Philipp, Holstein, Joshua, Hemmer, Patrick, Vössing, Michael, Kühl, Niklas, Martin, Dominik, Satzger, Gerhard·2025·Proceedings of the ACM on Human-Computer Interaction

Abstract

The integration of artificial intelligence (AI) into human decision-making processes at the workplace presents both opportunities and challenges. One promising approach to leverage existing complementary capabilities is allowing humans to delegate individual instances of decision tasks to AI. However, enabling humans to delegate instances effectively requires them to assess several factors. One key factor is the analysis of both their own capabilities and those of the AI in the context of the given task. In this work, we conduct a behavioral study to explore the effects of providing contextual information to support this delegation decision. Specifically, we investigate how contextual information about the AI and the task domain influence humans' delegation decisions to an AI and their impact on the human-AI team performance. Our findings reveal that access to contextual information significantly improves human-AI team performance in delegation settings. Finally, we show that the delegation behavior changes with the different types of contextual information. Overall, this research advances the understanding of computer-supported, collaborative work and provides actionable insights for designing more effective collaborative systems.

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1145/3710999

Cites

12 of 12 references verified0 citation errors · 0 not checkable

Statistics

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.

A few free checks a day · papers already verified open instantly · results are public

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?

  • Contextual factors such as task complexity and initiation mode of delegation shape whether consumers accept or resist autonomous AI decision-making.

    “Research also suggests that contextual factors, such as task complexity and initiation mode of delegation, shape whether consumers accept or resist autonomous AI decision-making (Adam et al. 2024;Spitzer et al. 2025).”

    From Machine marketing: rethinking the customer in the age of generative AI (2026) · cites this paper as Spitzer et al. 2025

    Section 5.3 examines whether perceived task difficulty and contextual information influence decisions to delegate to AI, and Section 6.1 reports that different kinds of contextual information change delegation behavior. The paper studies human delegation in workplace-oriented collaboration, not consumer acceptance or resistance, and the retrieved passages do not address who initiates delegation. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 65

  1. James D Abbey and Margaret G Meloy. 2017. Attention by design: Using attention checks to detect inattentive respondents and improve data quality. Journal of Operations Management 53 (2017), 63-70.
  2. Ashraf Abdul, Christian von der Weth, Mohan Kankanhalli, and Brian Y Lim. 2020. COGAM: measuring and moderating cognitive load in machine learning model explanations. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. 1-14. Manuscript submitted to ACM
  3. Albert Bandura. 1977. Self-efficacy: toward a unifying theory of behavioral change. Psychological review 84, 2 (1977), 191.
  4. Albert Bandura and Sebastian Wessels. 1997. Self-efficacy. Cambridge University Press Cambridge.
  5. Gagan Bansal, Besmira Nushi, Ece Kamar, Walter S Lasecki, Daniel S Weld, and Eric Horvitz. 2019. Beyond accuracy: The role of mental models in human-AI team performance. In Proceedings of the AAAI conference on human computation and crowdsourcing, Vol. 7. 2-11.
  6. Elizabeth Bondi, Raphael Koster, Hannah Sheahan, Martin Chadwick, Yoram Bachrach, Taylan Cemgil, Ulrich Paquet, and Krishnamurthy Dvijotham. 2022. Role of human-AI interaction in selective prediction. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 36. 5286-5294.
  7. Sebastian Bordt, Michèle Finck, Eric Raidl, and Ulrike von Luxburg. 2022. Post-hoc explanations fail to achieve their purpose in adversarial contexts. In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency. 891-905.
  8. Federico Cabitza, Andrea Campagner, Riccardo Angius, Chiara Natali, and Carlo Reverberi. 2023. AI Shall Have No Dominion: On How to Measure Technology Dominance in AI-Supported Human Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (Hamburg, Germany) (CHI '23). Association for Computing Machinery, New York, NY, USA, Article 354, 20 pages.
  9. Ángel Alexander Cabrera, Adam Perer, and Jason I Hong. 2023. Improving human-AI collaboration with descriptions of AI behavior. arXiv preprint arXiv:2301.06937 (2023). · arXiv
  10. Carrie J. Cai, Samantha Winter, David Steiner, Lauren Wilcox, and Michael Terry. 2019. "Hello AI": Uncovering the Onboarding Needs of Medical Practitioners for Human-AI Collaborative Decision-Making. Proc. ACM Hum.-Comput. Interact. 3, CSCW, Article 104 (nov 2019), 24 pages.
  11. Peter Cardon, Carolin Fleischmann, Jolanta Aritz, Minna Logemann, and Jeanette Heidewald. 2023. The Challenges and Opportunities of AI-Assisted Writing: Developing AI Literacy for the AI Age. Business and Professional Communication Quarterly (2023), 23294906231176517.
  12. Chun-Wei Chiang and Ming Yin. 2021. You'd Better Stop! Understanding Human Reliance on Machine Learning Models under Covariate Shift. In Proceedings of the 13th ACM Web Science Conference 2021 (Virtual Event, United Kingdom) (WebSci '21). Association for Computing Machinery, New York, NY, USA, 120-129.
  13. Leah Chong, Guanglu Zhang, Kosa Goucher-Lambert, Kenneth Kotovsky, and Jonathan Cagan. 2022. Human confidence in artificial intelligence and in themselves: The evolution and impact of confidence on adoption of AI advice. Computers in Human Behavior 127 (2022), 107018.
  14. Frances Ding, Moritz Hardt, John Miller, and Ludwig Schmidt. 2021. Retiring adult: New datasets for fair machine learning. Advances in neural information processing systems 34 (2021), 6478-6490.
  15. Upol Ehsan, Samir Passi, Q Vera Liao, Larry Chan, I Lee, Michael Muller, Mark O Riedl, et al. 2021. The who in explainable ai: How ai background shapes perceptions of ai explanations. arXiv preprint arXiv:2107.13509 (2021). · arXiv
  16. Martin Eppler and Jeanne Mengis. 2004. The Concept of Information Overload: A Review of Literature From Organization Science, Accounting, Marketing, MIS, and Related Disciplines. Inf. Soc. 20 (11 2004), 325-344.
  17. Yahya H Ezzeldin, Shen Yan, Chaoyang He, Emilio Ferrara, and A Salman Avestimehr. 2023. Fairfed: Enabling group fairness in federated learning. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 37. 7494-7502.
  18. Juliana Jansen Ferreira and Mateus Monteiro. 2021. The human-AI relationship in decision-making: AI explanation to support people on justifying their decisions. arXiv preprint arXiv:2102.05460 (2021). · arXiv
  19. Andreas Fuegener, Joern Grahl, Alok Gupta, Wolfgang Ketter, and Anna Taudien. 2021. Exploring User Heterogeneity in Human Delegation Behavior towards AI. In ICIS 2021 Proceedings.
  20. Andreas Fügener, Jörn Grahl, Alok Gupta, and Wolfgang Ketter. 2021. Will humans-in-the-loop become borgs? Merits and pitfalls of working with AI. Management Information Systems Quarterly (MISQ)-Vol 45 (2021).
  21. Andreas Fügener, Jörn Grahl, Alok Gupta, and Wolfgang Ketter. 2022. Cognitive challenges in human-artificial intelligence collaboration: Investigating the path toward productive delegation. Information Systems Research 33, 2 (2022), 678-696.
  22. Frederic Gmeiner, Humphrey Yang, Lining Yao, Kenneth Holstein, and Nikolas Martelaro. 2023. Exploring Challenges and Opportunities to Support Designers in Learning to Co-Create with AI-Based Manufacturing Design Tools. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (Hamburg, Germany) (CHI '23). Association for Computing Machinery, New York, NY, USA, Article 226, 20 pages.
  23. Frank Goldhammer, Johannes Naumann, Annette Stelter, Krisztina Tóth, Heiko Rölke, and Eckhard Klieme. 2014. The time on task effect in reading and problem solving is moderated by task difficulty and skill: insights from a computer-based large-scale assessment. Journal of Educational Psychology 106, 3 (2014), 608.
  24. Andrew F Hayes and Kristopher J Preacher. 2014. Statistical mediation analysis with a multicategorical independent variable. British journal of mathematical and statistical psychology 67, 3 (2014), 451-470.
  25. Patrick Hemmer, Sebastian Schellhammer, Michael Vössing, Johannes Jakubik, and Gerhard Satzger. 2022. Forming Effective Human-AI Teams: Building Machine Learning Models that Complement the Capabilities of Multiple Experts. In Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22. International Joint Conferences on Artificial Intelligence Organization, 2478-2484.
  26. Patrick Hemmer, Max Schemmer, Niklas Kühl, Michael Vössing, and Gerhard Satzger. 2022. On the effect of information asymmetry in human-AI teams. arXiv preprint arXiv:2205.01467 (2022). · arXiv
  27. Patrick Hemmer, Max Schemmer, Niklas Kühl, Michael Vössing, and Gerhard Satzger. 2024. Complementarity in Human-AI Collaboration: Concept, Sources, and Evidence. arXiv:2404.00029 · arXiv
  28. Patrick Hemmer, Max Schemmer, Michael Vössing, and Niklas Kühl. 2021. Human-AI Complementarity in Hybrid Intelligence Systems: A Structured Literature Review. PACIS (2021), 78.
  29. Patrick Hemmer, Monika Westphal, Max Schemmer, Sebastian Vetter, Michael Vössing, and Gerhard Satzger. 2023. 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. 453-463.
  30. Lukas-Valentin Herm. 2023. Impact Of Explainable AI On Cognitive Load: Insights From An Empirical Study. arXiv preprint arXiv:2304.08861 (2023). · arXiv
  31. Joshua Holstein, Patrick Müller, Michael Vössing, and Hansjoerg Fromm. 2025. Designing AI Assistants for Novices: Bridging Knowledge Gaps in Onboarding. Hawaii International Conference on Systems Sciences (HICSS-58) (2025).
  32. Joshua Holstein, Philipp Spitzer, Marieke Hoell, Michael Vössing, and Niklas Kühl. 2024. Understanding Data Understanding: A Framework to Navigate the Intricacies of Data Analytics. arXiv preprint arXiv:2405.07658 (2024). · arXiv
  33. Kenneth Holstein, Maria De-Arteaga, Lakshmi Tumati, and Yanghuidi Cheng. 2023. Toward supporting perceptual complementarity in human-AI collaboration via reflection on unobservables. Proceedings of the ACM on Human-Computer Interaction 7, CSCW1 (2023), 1-20.
  34. Antoine Hudon, Théophile Demazure, Alexander Karran, Pierre-Majorique Léger, and Sylvain Sénécal. 2021. Explainable artificial intelligence (XAI): how the visualization of AI predictions affects user cognitive load and confidence. In Information Systems and Neuroscience: NeuroIS Retreat 2021. Springer, 237-246.
  35. Anna Kawakami, Luke Guerdan, Yanghuidi Cheng, Kate Glazko, Matthew Lee, Scott Carter, Nikos Arechiga, Haiyi Zhu, and Kenneth Holstein. 2023. Training Towards Critical Use: Learning to Situate AI Predictions Relative to Human Knowledge. In Proceedings of The ACM Collective Intelligence Conference (CI '23). Association for Computing Machinery, New York, NY, USA, 63-78.
  36. Melina Klepsch, Florian Schmitz, and Tina Seufert. 2017. Development and validation of two instruments measuring intrinsic, extraneous, and germane cognitive load. Frontiers in psychology 8 (2017), 1997.
  37. Andy Kukla. 1974. Performance as a function of resultant achievement motivation (perceived ability) and perceived difficulty. Journal of Research in Personality 7, 4 (1974), 374-383.
  38. Diogo Leitão, Pedro Saleiro, Mário AT Figueiredo, and Pedro Bizarro. 2022. Human-AI collaboration in decision-making: beyond learning to defer. arXiv preprint arXiv:2206.13202 (2022). · arXiv
  39. Xiao-Yang Liu, Hongyang Yang, Jiechao Gao, and Christina Dan Wang. 2022. FinRL: Deep Reinforcement Learning Framework to Automate Trading in Quantitative Finance. In Proceedings of the Second ACM International Conference on AI in Finance (Virtual Event) (ICAIF '21). Association for Computing Machinery, New York, NY, USA, Article 1, 9 pages.
  40. Brian Lubars and Chenhao Tan. 2019. Ask not what AI can do, but what AI should do: Towards a framework of task delegability. Advances in Neural Information Processing Systems 32 (2019).
  41. Fred C Lunenburg. 2011. Self-efficacy in the workplace: Implications for motivation and performance. International journal of management, business, and administration 14, 1 (2011), 1-6.
  42. Allen E Milewski and Steven H Lewis. 1997. Delegating to software agents. International Journal of Human-Computer Studies 46, 4 (1997), 485-500.
  43. Suzanne M Miller. 1979. Controllability and human stress: Method, evidence and theory. Behaviour research and therapy 17, 4 (1979), 287-304.
  44. Sina Mohseni, Niloofar Zarei, and Eric D. Ragan. 2021. A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems. ACM Trans. Interact. Intell. Syst. 11, 3-4, Article 24 (sep 2021), 45 pages.
  45. Katelyn Morrison, Philipp Spitzer, Violet Turri, Michelle Feng, Niklas Kühl, and Adam Perer. 2024. The Impact of Imperfect XAI on Human-AI Decision-Making. Proceedings of the ACM on Human-Computer Interaction 8, CSCW1 (2024), 1-39.
  46. Hussein Mozannar and David Sontag. 2020. Consistent estimators for learning to defer to an expert. In International Conference on Machine Learning. PMLR, 7076-7087.
  47. Nastaran Okati, Abir De, and Manuel Rodriguez. 2021. Differentiable learning under triage. Advances in Neural Information Processing Systems 34 (2021), 9140-9151.
  48. Daniel M Oppenheimer, Tom Meyvis, and Nicolas Davidenko. 2009. Instructional manipulation checks: Detecting satisficing to increase statistical power. Journal of experimental social psychology 45, 4 (2009), 867-872.
  49. Rohan Paleja, Muyleng Ghuy, Nadun Ranawaka Arachchige, Reed Jensen, and Matthew Gombolay. 2021. The utility of explainable ai in ad hoc human-machine teaming. Advances in neural information processing systems 34 (2021), 610-623.
  50. Eyal Peer, Laura Brandimarte, Sonam Samat, and Alessandro Acquisti. 2017. Beyond the Turk: Alternative platforms for crowdsourcing behavioral research. Journal of experimental social psychology 70 (2017), 153-163.
  51. Marc Pinski, Martin Adam, and Alexander Benlian. 2023. AI Knowledge: Improving AI Delegation through Human Enablement. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. 1-17.
  52. Marc Pinski and Alexander Benlian. 2023. AI Literacy-Towards Measuring Human Competency in Artificial Intelligence. In Hawaii International Conference on Systems Sciences (HICSS-56).
  53. Muhammad Raees, Vassilis-Javed Khan, and Konstantinos Papangelis. 2023. Four Challenges for IML Designers: Lessons of an Interactive Customer Segmentation Prototype in a Global Manufacturing Company. In Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems (Hamburg, Germany) (CHI EA '23). Association for Computing Machinery, New York, NY, USA, Article 143, 6 pages.
  54. Maithra Raghu, Katy Blumer, Greg Corrado, Jon Kleinberg, Ziad Obermeyer, and Sendhil Mullainathan. 2019. The algorithmic automation problem: Prediction, triage, and human effort. arXiv preprint arXiv:1903.12220 (2019). · arXiv
  55. Max Schemmer, Joshua Holstein, Niklas Bauer, Niklas Kühl, and Gerhard Satzger. 2023. Towards meaningful anomaly detection: The effect of counterfactual explanations on the investigation of anomalies in multivariate time series. arXiv preprint arXiv:2302.03302 (2023). · arXiv
  56. Max Schemmer, Niklas Kühl, Carina Benz, and Gerhard Satzger. 2022. On the Influence of Explainable AI on Automation Bias. In Proceedings of the 30th European Conference on Information Systems (ECIS), Timis , oara, RO, June 18-24, 2022.
  57. Philipp Spitzer, Niklas Kühl, Marc Goutier, Manuel Kaschura, and Gerhard Satzger. 2024. Transferring Domain Knowledge with (X) AI-Based Learning Systems. arXiv preprint arXiv:2406.01329 (2024). · arXiv
  58. Mary Steffel and Elanor F Williams. 2018. Delegating decisions: Recruiting others to make choices we might regret. Journal of Consumer Research 44, 5 (2018), 1015-1032.
  59. Victor J Strecher, Brenda McEvoy DeVellis, Marshall H Becker, and Irwin M Rosenstock. 1986. The role of self-efficacy in achieving health behavior change. Health education quarterly 13, 1 (1986), 73-92.
  60. Steve G. Sutton, Vicky Arnold, and Matthew Holt. 2023. An extension of the theory of technology dominance: Capturing the underlying causal complexity. International Journal of Accounting Information Systems 50 (2023), 100626.
  61. John Sweller. 1994. Cognitive load theory, learning difficulty, and instructional design. Learning and instruction 4, 4 (1994), 295-312.
  62. Anna Taudien, Andreas Fuegener, Alok Gupta, and Wolfgang Ketter. 2022. Calibrating Users' Mental Models for Delegation to AI. In ICIS 2022 Proceedings.
  63. Bryan Wilder, Eric Horvitz, and Ece Kamar. 2020. Learning to complement humans. arXiv preprint arXiv:2005.00582 (2020). · arXiv
  64. Xiyang Zhang, Jing Qian, Bin Wang, Zhuyun Jin, Jiachen Wang, and Yu Wang. 2017. Leaders' behaviors matter: The role of delegation in promoting employees' feedback-seeking behavior. Frontiers in Psychology 8 (2017), 920.
  65. Xinshu Zhao, John G Lynch Jr, and Qimei Chen. 2010. Reconsidering Baron and Kenny: Myths and truths about mediation analysis. Journal of consumer research 37, 2 (2010), 197-206.

Cited by

Sorted by citations

Authors
Spitzer, P., Holstein, J., Hemmer, P., Vössing, M., Kühl, N., Martin, D., & Satzger, G.
Affiliations
  • Karlsruhe Institute of Technology (DE)
  • University of Bayreuth (DE)
Venue
Proceedings of the ACM on Human-Computer Interaction
Published
2025-05-02
Volume
9
Issue
2
Pages
1-28
ISSN
2573-0142 (ISSN-L)
OpenAlex
W4410061029
Cited by
18
References
12
Citations by year
2026: 14 · 2025: 4
FWCI
19.79
Type
journal article
Language
en
Open access
Locations
  • Proceedings of the ACM on Human-Computer Interaction (publishedVersion)
  • arXiv (Cornell University) (submittedVersion, cc-by-nc-nd)
  • arXiv (Cornell University)
Topics
  • Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
  • Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
  • Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
Keywords
human-AI collaboration, AI delegation, contextual information, computer-supported cooperative work, workplace decision-making

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