The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
“Human-AI collaboration: the effect of AI delegation on human task performance and task satisfaction,”
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
“In others, the AI agent has the decision to delegate and the user is assigned some of the decision making tasks to be completed on their own (Hemmer et al., 2023;Fügener et al., 2022), or presented with the AI's outcome as support, resulting in the AIfirst pattern (Bondi et al., 2022).”
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 Hemmer et al., 2023
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
Faul, F., Erdfelder, E., Lang, A.-G., & Buchner, A. (2007) · Behavior Research Methods · doi:10.3758/bf03193146 · Cited by 65,971
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
Adadi, A., & Berrada, M. (2018) · IEEE Access · doi:10.1109/access.2018.2870052 · Cited by 6,294
A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play
Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., Lanctot, M., Sifre, L., Kumaran, D., Graepel, T., et al. (2018) · Science · doi:10.1126/science.aar6404 · Cited by 3,562 · cited 1×
“Nowadays, the performance of AI models is similar to, and in certain application areas even exceeds the performance of human experts [12, 29, 64].”
BackgroundAlgorithm aversion: People erroneously avoid algorithms after seeing them err.
Dietvorst, B. J., Simmons, J. P., & Massey, C. (2015) · Journal of Experimental Psychology: General · doi:10.1037/xge0000033 · Cited by 3,232
Self-efficacy: implications for organizational behavior and human resource management
Gist, M. E. (1987) · The Academy of Management Review · doi:10.5465/amr.1987.4306562 · Cited by 1,447
Buçinca, Z., Malaya, M. B., & Gajos, K. Z. (2021) · arXiv · doi:10.48550/arxiv.2102.09692 · Cited by 1,124
Zhang, Y., Liao, Q. V., & Bellamy, R. K. E. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency · doi:10.1145/3351095.3372852 · Cited by 895
Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance
Bansal, G., Wu, T., Zhou, J., Fok, R., Nushi, B., Kamar, E., Ribeiro, M. T., & Weld, D. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445717 · Cited by 757 · cited 3×
“These potentially complementary capabilities motivated researchers to investigate how the abilities of humans and AI models can be combined to further improve overall decision-making performance [6, 33].”
BackgroundDoes the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance
Bansal, G., Wu, T., Zhou, J., Fok, R., Nushi, B., Kamar, E., Ribeiro, M. T., & Weld, D. S. (2020) · arXiv · doi:10.48550/arxiv.2006.14779 · Cited by 754
Superhuman AI for multiplayer poker
Brown, N., & Sandholm, T. (2019) · Science · doi:10.1126/science.aay2400 · Cited by 592
Lai, V., & Tan, C. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287590 · Cited by 377
Wang, X., & Yin, M. (2021) · 26th International Conference on Intelligent User Interfaces · doi:10.1145/3397481.3450650 · Cited by 320 · cited 1×
“and can, therefore, either accept or override the recommendation [61, 71].”
BackgroundGulshan, V., Rajan, R. P., Widner, K., Wu, D., Wubbels, P., Rhodes, T., Whitehouse, K., Coram, M., Corrado, G., Ramasamy, K., et al. (2019) · JAMA Ophthalmology · doi:10.1001/jamaophthalmol.2019.2004 · Cited by 303 · cited 1×
“For example, in the medical domain, AI models can detect certain diseases as accurately as radiologists [18, 27, 36].”
BackgroundAlgorithmic Management: Bright and Dark Sides, Practical Implications, and Research Opportunities
Benlian, A., Wiener, M., Cram, W. A., Krasnova, H., Maedche, A., Möhlmann, M., Recker, J., & Remus, U. (2022) · Business & Information Systems Engineering · doi:10.1007/s12599-022-00764-w · Cited by 180
Human-AI Collaboration via Conditional Delegation: A Case Study of Content Moderation
Lai, V., Carton, S., Bhatnagar, R., Liao, Q. V., Zhang, Y., & Tan, C. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3501999 · Cited by 145
"Why is 'Chicago' deceptive?" Towards Building Model-Driven Tutorials for Humans
Lai, V., Liu, H., & Tan, C. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376873 · Cited by 123
Nourani, M., King, J., & Ragan, E. (2020) · Proceedings of the AAAI Conference on Human Computation and Crowdsourcing · doi:10.1609/hcomp.v8i1.7469 · Cited by 116
Does Explainable Artificial Intelligence Improve Human Decision-Making?
Alufaisan, Y., Marusich, L. R., Bakdash, J. Z., Zhou, Y., & Kantarcioglu, M. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i8.16819 · Cited by 106
An Animal Detection Pipeline for Identification
Parham, J., Stewart, C., Crall, J., Rubenstein, D., Holmberg, J., & Berger-Wolf, T. (2018) · 2018 IEEE Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv.2018.00123 · Cited by 103
What can AI do for me?: evaluating machine learning interpretations in cooperative play
Feng, S., & Boyd-Graber, J. (2019) · Proceedings of the 24th International Conference on Intelligent User Interfaces · doi:10.1145/3301275.3302265 · Cited by 92
A Meta-Analysis of the Utility of Explainable Artificial Intelligence in Human-AI Decision-Making
Schemmer, M., Hemmer, P., Nitsche, M., Kühl, N., & Vössing, M. (2022) · Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3514094.3534128 · Cited by 91
Self-citationFeature-Based Explanations Don't Help People Detect Misclassifications of Online Toxicity
Carton, S., Mei, Q., & Resnick, P. (2020) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v14i1.7282 · Cited by 58
Towards Unbiased and Accurate Deferral to Multiple Experts
Keswani, V., Lease, M., & Kenthapadi, K. (2021) · Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3461702.3462516 · Cited by 45 · cited 4×
“proposed that can either complement the capabilities of a single [50, 54, 56, 72] or multiple [31, 39] humans who are part of the human-AI team.”
BackgroundInfluentialShould I Follow AI-based Advice? Measuring Appropriate Reliance in Human-AI Decision-Making
Schemmer, M., Hemmer, P., Kühl, N., Benz, C., & Satzger, G. (2022) · arXiv · doi:10.48550/arxiv.2204.06916 · Cited by 33 · cited 1×
“Establishing an appropriate level of reliance on the AI model becomes one of the central challenges [60].”
BackgroundSelf-citation
Research works printed but not linked: 54
- 10.1108/JMD-03-2019-0076 · DOI
- 10.5296/ijhrs.v5i3.8062 · doi:10.5296/ijhrs.v5i3.8062
- 10.1037/0033-295X.84.2.191 · DOI · Paper page
- Bernard M Bass and Ralph Melvin Stogdill. 1990. Bass & Stogdill’s Handbook of Leadership: Theory, Research, and Managerial Applications. Simon and Schuster.
- Roy F Baumeister, Kathleen D Vohs, 2002. The pursuit of meaningfulness in life. Handbook of Positive Psychology 1 (2002), 608–618.
- Mohsin Bilal, Yee Wah Tsang, Mahmoud Ali, Simon Graham, Emily Hero, Noorul Wahab, Katherine Dodd, Harvir Sahota, Wenqi Lu, Mostafa Jahanifar, 2022. AI based pre-screening of large bowel cancer via weakly supervised learning of colorectal biopsy histology images. medRxiv (2022).
- 10.1609/aaai.v36i5.20465 · DOI · Paper page
- 10.1145/3377325.3377498 · DOI · Paper page
- 10.1145/3449287 · doi:10.1145/3449287 · Paper page
- 10.1145/3397481.3450698 · DOI
- Andre Esteva, Brett Kuprel, Roberto A Novoa, Justin Ko, Susan M Swetter, Helen M Blau, and Sebastian Thrun. 2017. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 7639 (2017), 115–118.
- 10.5465/ambpp.1980.4977898 · doi:10.5465/ambpp.1980.4977898
- 10.1287/isre.2021.1079 · DOI · Paper page
- 10.1146/annurev.so.08.080182.000245 · DOI · Paper page
- Robert Geirhos Kantharaju Narayanappa Benjamin Mitzkus Tizian Thieringer Matthias Bethge Felix A Wichmann and Wieland Brendel. 2021. Partial success in closing the gap between human and machine vision. In Advances in Neural Information Processing Systems. 23885–23899.
- Cornelia Gerdenitsch. 2017. New ways of working and satisfaction of psychological needs. In Job Demands in a Changing World of Work. 91–109. · DOI
- 10.2307/258514 · doi:10.2307/258514 · Paper page
- Andrew F Hayes. 2017. Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-based Approach. Guilford publications.
- 10.1109/ICCV.2015.123 · DOI · Paper page
- 10.1016/j.ejca.2019.07.019 · DOI · Paper page
- 10.24963/ijcai.2022/344 · DOI · Paper page
- Patrick Hemmer, Max Schemmer, Niklas Kühl, Michael Vössing, and Gerhard Satzger. 2022. On the effect of information asymmetry in human-AI teams. Human-Centered Explainable AI Workshop at the 2022 CHI Conference on Human Factors in Computing Systems (2022).
- Patrick Hemmer, Max Schemmer, Michael Vössing, and Niklas Kühl. 2021. Human-AI complementarity in hybrid intelligence systems: a structured literature review. In Proceedings of the Pacific Asia Conference on Information Systems.
- 10.1111/j.1559-1816.1995.tb01764.x · DOI
- 10.1109/CVPR.2017.243 · DOI · Paper page
- 10.1609/aaai.v33i01.3301590 · DOI · Paper page
- 10.1016/j.intell.2013.09.004 · DOI
- Gavin Kerrigan Padhraic Smyth and Mark Steyvers. 2021. Combining human predictions with model probabilities via confusion matrices and calibration. In Advances in Neural Information Processing Systems. 4421–4434.
- 10.2307/255943 · DOI
- Carrie R Leana. 1987. Power relinquishment versus power sharing: theoretical clarification and empirical comparison of delegation and participation.Journal of Applied Psychology 72, 2 (1987), 228.
- 10.1145/2702123.2702548 · DOI · Paper page
- Diogo Leitão, Pedro Saleiro, Mário AT Figueiredo, and Pedro Bizarro. 2022. Human-AI collaboration in decision-making: beyond learning to defer. Workshop on Human-Machine Collaboration and Teaming at the International Conference on Machine Learning (2022).
- 10.1037/0021-9010.69.2.241 · DOI · Paper page
- 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.
- Mareike Möhlmann and Ola Henfridsson. 2019. What people hate about being managed by algorithms, according to a study of Uber drivers. Harvard Business Review 30 (2019), 1–7.
- Hussein Mozannar and David Sontag. 2020. Consistent estimators for learning to defer to an expert. In Proceedings of the 37th International Conference on Machine Learning. 7076–7087.
- Giang Nguyen Daeyoung Kim and Anh Nguyen. 2021. The effectiveness of feature attribution methods and its correlation with automatic evaluation scores. In Advances in Neural Information Processing Systems. 26422–26436.
- Niilo Noponen. 2019. Impact of artificial intelligence on management. Electronic Journal of Business Ethics and Organization Studies 24, 2 (2019).
- Nastaran Okati Abir De and Manuel Rodriguez. 2021. Differentiable learning under triage. In Advances in Neural Information Processing Systems. 9140–9151.
- 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
- 10.1145/3512930 · DOI · Paper page
- 10.1007/s11263-015-0816-y · DOI · Paper page
- 10.1145/3490099.3511128 · DOI
- 10.2307/256909 · DOI · Paper page
- 10.1177/001872678904200705 · DOI · Paper page
- 10.2307/256865 · DOI · Paper page
- 10.1037/0033-2909.124.2.240 · DOI · Paper page
- 10.1073/pnas.2111547119 · DOI
- John Ugoani. 2020. Effective delegation and its impact on employee performance. International Journal of Economics and Business Administration 6, 3 (2020), 78–87.
- 10.1016/j.artint.2020.103404 · DOI · Paper page
- 10.1007/s10796-022-10284-3 · DOI · Paper page
- 10.24963/ijcai.2020/212 · DOI · Paper page
- 10.5465/amj.2007.24162389 · DOI · Paper page
- 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), 1–10.
Cited by
Agentic AI: Autonomous intelligence for complex goals–a comprehensive survey
Acharya, D. B., Kuppan, K., & Divya, B. (2025) · IEEE Access · doi:10.1109/access.2025.3532853 · Cited by 717
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
Williamson, S. M., & Prybutok, V. (2024) · Information · doi:10.3390/info15060299 · Cited by 146
Enhancing AI-Assisted Group Decision Making through LLM-Powered Devil's Advocate
Chiang, C.-W., Lu, Z., Li, Z., & Yin, M. (2024) · Proceedings of the 29th International Conference on Intelligent User Interfaces · doi:10.1145/3640543.3645199 · Cited by 142
He, G., Demartini, G., & Gadiraju, U. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713218 · Cited by 23
Metadata
- Authors
- Hemmer, P., Westphal, M., Schemmer, M., Vetter, S., Vössing, M., & Satzger, G.
- Affiliations
- Karlsruhe Institute of Technology, Germany
- Ben-Gurion University of the Negev, Israel
- Venue
- Proceedings of the 28th International Conference on Intelligent User Interfaces
- Publisher
- ACM
- Published
- 2023-03-27
- Pages
- 453-463
- Cited by
- 93
- References
- 24
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
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.