journal article·2023-12-05Open access

Complexity-Driven Trust Dynamics in Human–Robot Interactions: Insights from AI-Enhanced Collaborative Engagements

Yi Zhu, Taotao Wang, Chang Wang, Wei Quan, Mingwei Tang

Applied Sciences· Volume 13· Issue 24

Abstract

This study explores the intricate dynamics of trust in human–robot interaction (HRI), particularly in the context of modern robotic systems enhanced by artificial intelligence (AI). By grounding our investigation in the principles of interpersonal trust, we identify and analyze both similarities and differences between trust in human–human interactions and human–robot scenarios. A key aspect of our research is the clear definition and characterization of trust in HRI, including the identification of factors influencing its development. Our empirical findings reveal that trust in HRI is not static but varies dynamically with the complexity of the tasks involved. Notably, we observe a stronger tendency to trust robots in tasks that are either very straightforward or highly complex. In contrast, for tasks of intermediate complexity, there is a noticeable decline in trust. This pattern of trust challenges conventional perceptions and emphasizes the need for nuanced understanding and design in HRI. Our study provides new insights into the nature of trust in HRI, highlighting its dynamic nature and the influence of task complexity, thereby offering a valuable reference for future research in the field.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Human-Automation Interaction and Safety · Cognitive Functions and Memory · Ethics and Social Impacts of AI

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

School of Computer Science, Nanjing Audit University, Nanjing 211800, China

College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China

School of Statistics and Data Science, Nanjing Audit University, Nanjing 211800, China

Funding

National Natural Science Foundation of China · National Social Science Fund of China · Significant Project of Jiangsu College Philosophy and Social Sciences Research · Jiangsu College Philosophy and Social Sciences Research · Planning Fund Project of Humanities and Social Sciences Research of Ministry of Education of China

References (57)

Show source-provided references
1. Human-in-the-Loop Optimization of Wearable Robotic Devices to Improve Human-Robot Interaction: A Systematic Review
2. Ling, H., Liu, G., Zhu, L., Huang, B., Lu, F., Wu, H., Tian, G., and Ji, Z. (2022, January 27–31). Motion Planning Combines Human Motion Prediction for Human-Robot Cooperation. Proceedings of the 2022 12th International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER), Baishan, China.10.1109/CYBER55403.2022.9907516 ↗
3. Wang, C., Wen, X., Niu, Y., Wu, L., Yin, D., and Li, J. (December, January 30). Dynamic task allocation for heterogeneous manned-unmanned aerial vehicle teamwork. Proceedings of the 2018 Chinese Automation Congress (CAC), Xi’an, China.10.1109/CAC.2018.8623480 ↗
4. Three Strikes and you are out!: The impacts of multiple human-robot trust violations and repairs on robot trustworthiness10.1016/j.chb.2023.107658 ↗
5. The trustworthy and acceptable HRI checklist (TA-HRI): Questions and design recommendations to support a trust-worthy and acceptable design of human-robot interaction10.1007/s11612-022-00643-8 ↗
6. Guo, T., Obidat, O., Rodriguez, L., Parron, J., and Wang, W. (October, January 30). Reasoning the Trust of Humans in Robots through Physiological Biometrics in Human-Robot Collaborative Contexts. Proceedings of the 2022 IEEE MIT Undergraduate Research Technology Conference (URTC), Cambridge, MA, USA.10.1109/URTC56832.2022.10002210 ↗
7. Human-Robot Teams: A Discussion of the Emerging Trends10.1177/1071181322661005 ↗
8. Capturing Dynamic Trust Metrics during Shared Space Human Robot Collaboration: An eye-tracking approach10.1177/1071181322661296 ↗
9. Coactive design of explainable agent-based task planning and deep reinforcement learning for human-UAVs teamwork10.1016/j.cja.2020.05.001 ↗
10. Applications of unmanned aerial vehicle (UAV) in road safety, traffic and highway infrastructure management: Recent advances and challenges10.1016/j.tra.2020.09.018 ↗
11. Driggs, J., and Vangsness, L. (2022, January 17–19). Changes in Trust in Automation (TIA) After Performing a Visual Search Task with an Automated System. Proceedings of the 2022 IEEE 3rd International Conference on Human-Machine Systems (ICHMS), Orlando, FL, USA.10.1109/ICHMS56717.2022.9980724 ↗
12. Interpersonal trust, trustworthiness, and gullibility10.1037/0003-066X.35.1.1 ↗
13. Cultural intelligence and innovative work behavior: The role of work engagement and interpersonal trust10.1108/EJIM-01-2020-0008 ↗
14. Spadaro, G., Gangl, K., Van Prooijen, J.-W., Van Lange, P.A., and Mosso, C.O. (2020). Enhancing feelings of security: How institutional trust promotes interpersonal trust. PLoS ONE, 15.10.1371/journal.pone.0237934 ↗
15. Project team resilience: The effect of group potency and interpersonal trust10.1016/j.ijproman.2021.06.004 ↗
16. Different roles of interpersonal trust and institutional trust in COVID-19 pandemic control10.1016/j.socscimed.2021.114677 ↗
17. Human–computer interaction-based decision support system with applications in data mining10.1016/j.future.2020.07.048 ↗
18. A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques10.1109/ACCESS.2021.3127881 ↗
19. Ho, Y.-H., and Tsai, Y.-J. (2022). Open collaborative platform for multi-drones to support search and rescue operations. Drones, 6.10.3390/drones6050132 ↗
20. Gupta, R., Shukla, A., Mehta, P., Bhattacharya, P., Tanwar, S., Tyagi, S., and Kumar, N. (2020, January 6–9). VAHAK: A blockchain-based outdoor delivery scheme using UAV for healthcare 4.0 services. Proceedings of the IEEE INFOCOM 2020-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), Toronto, ON, Canada.10.1109/INFOCOMWKSHPS50562.2020.9162738 ↗
21. Zuhair, M., Patel, F., Navapara, D., Bhattacharya, P., and Saraswat, D. (2021, January 28–30). BloCoV6: A blockchain-based 6G-assisted UAV contact tracing scheme for COVID-19 pandemic. Proceedings of the 2021 2nd International Conference on Intelligent Engineering and Management (ICIEM), London, UK.10.1109/ICIEM51511.2021.9445332 ↗
22. Sahoo, S.K., Mudligiriyappa, N., Algethami, A.A., Manoharan, P., Hamdi, M., and Raahemifar, K. (2022). Intelligent trust-based utility and reusability model: Enhanced security using unmanned aerial vehicles on sensor nodes. Appl. Sci., 12.10.3390/app12031317 ↗
23. Ko, Y., Kim, J., Duguma, D.G., Astillo, P.V., You, I., and Pau, G. (2021). Drone secure communication protocol for future sensitive applications in military zone. Sensors, 21.10.3390/s21062057 ↗
24. Blockchain-envisioned softwarized multi-swarming UAVs to tackle COVID-I9 situations10.1109/MNET.011.2000439 ↗
25. Rapid trust calibration through interpretable and uncertainty-aware AI10.1016/j.patter.2020.100049 ↗
26. Huang, R., Zhou, H., Liu, T., and Sheng, H. (2022). Multi-UAV collaboration to survey Tibetan antelopes in Hoh Xil. Drones, 6.10.3390/drones6080196 ↗
27. Machines as teammates: A research agenda on AI in team collaboration10.1016/j.im.2019.103174 ↗
28. Trust as a social reality10.2307/2578601 ↗
29. Can we trust trust
30. Ciocirlan, S.-D., Agrigoroaie, R., and Tapus, A. (2019, January 14–18). Human-robot team: Effects of communication in analyzing trust. Proceedings of the 2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), New Delhi, India.10.1109/RO-MAN46459.2019.8956345 ↗
31. Yuan, X., Olfman, L., and Yi, J. (2020). Information Diffusion Management and Knowledge Sharing: Breakthroughs in Research and Practice, IGI Global.
32. Trust and risk perception: A critical review of the literature10.1111/risa.13325 ↗
33. Improving human-machine collaboration through transparency-based feedback–part I: Human trust and workload model
34. Lu, Z., Guo, J., Zeng, S., and Mao, Q. (2020, January 25–27). Research on Human-machine Dynamic Trust Based on Alarm Sequence. Proceedings of the Proceedings of the 2nd World Symposium on Software Engineering, Chengdu, China.10.1145/3425329.3425389 ↗
35. Systematic review: Trust-building factors and implications for conversational agent design10.1080/10447318.2020.1807710 ↗
36. Okamura, K., and Yamada, S. (2020). Adaptive trust calibration for human-AI collaboration. PLoS ONE, 15.10.1371/journal.pone.0229132 ↗
37. Individual differences in attributes of trust in automation: Measurement and application to system design10.3389/fpsyg.2019.01117 ↗
38. Trust and compliance to public health policies in times of COVID-1910.1016/j.jpubeco.2020.104316 ↗
39. Human-machine collaboration in managerial decision making10.1016/j.chb.2021.106730 ↗
40. Dynamic trust emergency role-based access control (DTE–RBAC)
41. A Qualitative Analysis of Trust Dynamics in Human-Agent Teams (HATs)10.1177/1071181322661242 ↗
42. Jghef, Y.S., Jasim, M.J.M., Ghanimi, H.M., Algarni, A.D., Soliman, N.F., El-Shafai, W., Zeebaree, S.R., Alkhayyat, A., Abosinnee, A.S., and Abdulsattar, N.F. (2022). Bio-Inspired Dynamic Trust and Congestion-Aware Zone-Based Secured Internet of Drone Things (SIoDT). Drones, 6.10.3390/drones6110337 ↗
43. Rhim, J., Kwak, S.S., Lim, A., and Millar, J. (2023). The dynamic nature of trust: Trust in Human-Robot Interaction revisited. arXiv.
44. Toreini, E., Aitken, M., Coopamootoo, K., Elliott, K., Zelaya, C.G., and Van Moorsel, A. (2020, January 27–30). The relationship between trust in AI and trustworthy machine learning technologies. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, Barcelona, Spain.10.1145/3351095.3372834 ↗
45. A review on human–machine trust evaluation: Human-centric and machine-centric perspectives10.1109/THMS.2022.3144956 ↗
46. Cai, H., Wang, C., and Zhu, Y. (2021, January 15–17). The Influencing Factors of Human-Machine Trust: A Behavioral Science Perspective. Proceedings of the International Conference on Autonomous Unmanned Systems, Beijing, China.
47. Developing human-machine trust: Impacts of prior instruction and automation failure on driver trust in partially automated vehicles10.1016/j.trf.2021.06.013 ↗
48. Effects of personality traits on user trust in human–machine collaborations10.1007/s12193-020-00329-9 ↗
49. Examining students’ self-efficacy and perceptions of task difficulty in learning from multiple texts10.1016/j.lindif.2021.102052 ↗
50. Choosing task characteristics oneself justifies effort: A study on cardiac response and the critical role of task difficulty10.1037/mot0000269 ↗
51. Varga, B., Doer, C., Trommer, G.F., and Hohmann, S. (2022, January 25–28). Validation of a Limit Ellipsis Controller for Rescue Drones. Proceedings of the 2022 IEEE 16th International Symposium on Applied Computational Intelligence and Informatics (SACI), Timisoara, Romania.10.1109/SACI55618.2022.9919440 ↗
52. Yao, C., Xiaoling, L., Zhiyuan, L., Huifen, W., and Pengcheng, W. (2017, January 16–19). Research on the UAV multi-channel human-machine interaction system. Proceedings of the 2017 2nd Asia-Pacific Conference on Intelligent Robot Systems (ACIRS), Wuhan, China.10.1109/ACIRS.2017.7986091 ↗
53. Pham, D., Menon, V., Tenhundfeld, N.L., Weger, K., Mesmer, B.L., Gholston, S., and Davis, T. (2022, January 17–19). A Case Study of Human-AI Interactions Using Transparent AI-Driven Autonomous Systems for Improved Human-AI Trust Factors. Proceedings of the 2022 IEEE 3rd International Conference on Human-Machine Systems (ICHMS), Orlando, FL, USA.10.1109/ICHMS56717.2022.9980662 ↗
54. Xu, Y. (2022, January 23–25). Research of Flight Test Method for Manned/Unmanned Aerial Vehicle Cooperation Technology. Proceedings of the 2022 the 5th International Conference on Robot Systems and Applications (ICRSA), Shenzhen, China.10.1145/3556267.3556276 ↗
55. Noda, A., Harazono, Y., Ueda, K., Ishii, H., and Shimoda, H. (2022, January 9–12). A Study on 3D Reconstruction Method in Cooperation with a Mirror-mounted Autonomous Drone. Proceedings of the 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Prague, Czech Republic.10.1109/SMC53654.2022.9945519 ↗
56. Virtual agents and flow experience: An empirical examination of AI-powered chatbots10.1016/j.techfore.2022.121772 ↗
57. Johnson, M. (2014). Coactive Design: Designing Support for Interdependence in Human-Robot Teamwork. [Ph.D. Thesis, Delft University of Technology].10.5898/JHRI.3.1.Johnson ↗