The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
Intelligent Agent Transparency in Human–Agent Teaming for Multi-UxV Management
Open accessAbstract
Objective: We investigated the effects of level of agent transparency on operator performance, trust, and workload in a context of human–agent teaming for multirobot management. Background: Participants played the role of a heterogeneous unmanned vehicle (UxV) operator and were instructed to complete various missions by giving orders to UxVs through a computer interface. An intelligent agent (IA) assisted the participant by recommending two plans—a top recommendation and a secondary recommendation—for every mission. Method: A within-subjects design with three levels of agent transparency was employed in the present experiment. There were eight missions in each of three experimental blocks, grouped by level of transparency. During each experimental block, the IA was incorrect three out of eight times due to external information (e.g., commander’s intent and intelligence). Operator performance, trust, workload, and usability data were collected. Results: Results indicate that operator performance, trust, and perceived usability increased as a function of transparency level. Subjective and objective workload data indicate that participants’ workload did not increase as a function of transparency. Furthermore, response time did not increase as a function of transparency. Conclusion: Unlike previous research, which showed that increased transparency resulted in increased performance and trust calibration at the cost of greater workload and longer response time, our results support the benefits of transparency for performance effectiveness without additional costs. Application: The current results will facilitate the implementation of IAs in military settings and will provide useful data to the design of heterogeneous UxV teams.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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
“at least fve studies (see e.g., [4,130]) used the trust predisposition scale by McKnight et al. [91]”
From Measuring and Understanding Trust Calibrations for Automated Systems: A Survey of the State-Of-The-Art and Future Directions (2023) · cites this paper as [91]
In the citing paper
“However, we also noticed that only a few studies diferentiated between diferent subconstructs of trust (performance, process, purpose) which is inherently included in many scales (e.g., [91]) but authors did not make use of this information.”
From Measuring and Understanding Trust Calibrations for Automated Systems: A Survey of the State-Of-The-Art and Future Directions (2023) · cites this paper as [91]
In the citing paper
“Critically, the opaqueness of the AI's internal computational processes may decrease calibrated reliance on the system [63 , 64] .”
From Enhancing human-AI collaboration: The case of colonoscopy (2024) · cites this paper as [63]
In the citing paper
“Most of the studies investigated how to maintain appropriate trust calibration by continuously presenting system information to prevent over-trust or under-trust.”
From Adaptive trust calibration for human-AI collaboration (2020) · cites this paper as [12]
Citations
Cites
Artificial intelligence: a modern approach
Kumar, D. A., Balasubramanian, S., Byeon, D. H., & Manerkar, P. G. V. (1995) · Choice Reviews Online · doi:10.5860/choice.33-1577 · Cited by 22,191
Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research
(1988) · Advances in Psychology · doi:10.1016/s0166-4115(08)62386-9 · Cited by 15,003
Artificial intelligence: A modern approach
Nilsson, N. J. (1996) · Artificial Intelligence · doi:10.1016/0004-3702(96)00007-0 · Cited by 10,730
Toward a Theory of Situation Awareness in Dynamic Systems
Endsley, M. R. (1995) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1518/001872095779049543 · Cited by 8,875
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 · cited 8×
“The opera- tor’s SA of the IA’s intent, reasoning, projected outcomes, and uncertainty should improve the operator’s subjective trust as well as trust calibration, which is proper reliance when the IA is correct and correct rejection when the IA is incorrect (Lee & See, 2004).”
BackgroundMethodInfluentialHumans 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
Usability Evaluation In Industry
(1996) · CRC Press eBooks · doi:10.1201/9781498710411 · Cited by 4,445
A model for types and levels of human interaction with automation
Parasuraman, R., Sheridan, T. B., & Wickens, C. D. (2000) · IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans · doi:10.1109/3468.844354 · Cited by 4,195 · cited 1×
“…analysis (trust in the information and analysis displayed) and (b) decision and action selection (trust in the IA’s suggestions and decisions; Parasuraman, Sheridan, & Wickens, 2000), using a modified version of the Trust Between People and Automation questionnaire developed by Jian,…”
MethodWorking memory span tasks: A methodological review and user’s guide
Conway, A. R. A., Kane, M. J., Bunting, M. F., Hambrick, D. Z., Wilhelm, O., & Engle, R. W. (2005) · Psychonomic Bulletin & Review · doi:10.3758/bf03196772 · Cited by 2,882
Rayner, K. (2009) · Quarterly Journal of Experimental Psychology · doi:10.1080/17470210902816461 · Cited by 2,682
Research works printed but not linked: 5
- Douglass S., 2013, Design recommendations for adaptive intelligent tutoring systems learner modeling, 1, 111
- Draper M, H, 2013, Realizing autonomy via intelligent adaptive hybrid control: Adaptable autonomy for achieving UxV RSTA team decision superiority
- Ekstrom R. B., 1976, Manual for kit of factor-referenced cognitive tests
- Helldin T., 2014, Transparency for future semi-automated systems
- Spriggs S., 2014, Fusion system overview
Cited by
Sorted by citations
Explanation in artificial intelligence: Insights from the social sciences
Miller, T. (2019) · Artificial Intelligence · doi:10.1016/j.artint.2018.07.007 · Cited by 5,302
Schaefer, K. E., Chen, J. Y. C., Szalma, J. L., & Hancock, P. A. (2016) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720816634228 · Cited by 768
Self-citationHuman–Autonomy Teaming: A Review and Analysis of the Empirical Literature
O’Neill, T., McNeese, N. J., Barron, A. H., & Schelble, B. G. (2020) · Human Factors The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720820960865 · Cited by 681
Transparency in artificial intelligence
Larsson, S., & Heintz, F. (2020) · Internet Policy Review · doi:10.14763/2020.2.1469 · Cited by 458
Towards a Theory of Longitudinal Trust Calibration in Human–Robot Teams
de Visser, E. J., Peeters, M. M. M., Jung, M. F., Kohn, S., Shaw, T. H., Pak, R., & Neerincx, M. A. (2020) · International Journal of Social Robotics · doi:10.1007/s12369-019-00596-x · Cited by 447
From ‘automation’ to ‘autonomy’: the importance of trust repair in human–machine interaction
de Visser, E. J., Pak, R., & Shaw, T. H. (2018) · Ergonomics · doi:10.1080/00140139.2018.1457725 · Cited by 450
Evaluating XAI: A comparison of rule-based and example-based explanations
van der Waa, J., Nieuwburg, E., Cremers, A., & Neerincx, M. (2021) · Artificial Intelligence · doi:10.1016/j.artint.2020.103404 · Cited by 393
Explanations as Mechanisms for Supporting Algorithmic Transparency
Rader, E., Cotter, K., & Cho, J. (2018) · Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3173677 · Cited by 388
Situation awareness-based agent transparency and human-autonomy teaming effectiveness
Chen, J. Y. C., Lakhmani, S. G., Stowers, K., Selkowitz, A. R., Wright, J. L., & Barnes, M. (2018) · Theoretical Issues in Ergonomics Science · doi:10.1080/1463922x.2017.1315750 · Cited by 341
Self-citationSupporting Human-AI Teams:Transparency, explainability, and situation awareness
Endsley, M. R. (2022) · Computers in Human Behavior · doi:10.1016/j.chb.2022.107574 · Cited by 366
Adaptive trust calibration for human-AI collaboration
Okamura, K., & Yamada, S. (2020) · PLOS ONE · doi:10.1371/journal.pone.0229132 · Cited by 239
Evaluating Effects of User Experience and System Transparency on Trust in Automation
Yang, X. J., Unhelkar, V. V., Li, K., & Shah, J. A. (2017) · Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction · doi:10.1145/2909824.3020230 · Cited by 208
Vorm, E. S., & Combs, D. J. Y. (2022) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2022.2070107 · Cited by 232
Contrastive explanation: a structural-model approach
Miller, T. (2021) · The Knowledge Engineering Review · doi:10.1017/s0269888921000102 · Cited by 186
Challenges and opportunities for artificial intelligence in auditing: Evidence from the field
Kokina, J., Blanchette, S., Davenport, T. H., & Pachamanova, D. (2025) · International Journal of Accounting Information Systems · doi:10.1016/j.accinf.2025.100734 · Cited by 375
Du, N., Haspiel, J., Zhang, Q., Tilbury, D. M., Pradhan, A. K., Yang, X. J., & Robert, L. (2019) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2019.05.025 · Cited by 174
Kunze, A., Summerskill, S. J., Marshall, R., & Filtness, A. J. (2019) · Ergonomics · doi:10.1080/00140139.2018.1547842 · Cited by 165
van de Merwe, K., Mallam, S., & Nazir, S. (2022) · Human Factors The Journal of the Human Factors and Ergonomics Society · doi:10.1177/00187208221077804 · Cited by 172
Let's Think Together! Assessing Shared Mental Models, Performance, and Trust in Human-Agent Teams
Schelble, B. G., Flathmann, C., McNeese, N. J., Freeman, G., & Mallick, R. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3492832 · Cited by 177
Transparency and Explanation in Deep Reinforcement Learning Neural Networks
Iyer, R., Li, Y., Li, H., Lewis, M., Sundar, R., & Sycara, K. P. (2018) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3278721.3278776 · Cited by 154
Wischnewski, M., Krämer, N., & Müller, E. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581197 · Cited by 146
Understanding artificial intelligence ethics and safetyPreprint
Leslie, D. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.05684 · Cited by 308
Trustworthy AI and robotics: Implications for the AEC industry
Emaminejad, N., & Akhavian, R. (2022) · Automation in Construction · doi:10.1016/j.autcon.2022.104298 · Cited by 158
Communicating intent to develop shared situation awareness and engender trust in human-agent teams
Schaefer, K. E., Straub, E. R., Chen, J. Y. C., Putney, J., & Evans, A. W. (2017) · Cognitive Systems Research · doi:10.1016/j.cogsys.2017.02.002 · Cited by 136
Self-citationWeitz, K., Schiller, D., Schlagowski, R., Huber, T., & André, E. (2019) · Proceedings of the 19th ACM International Conference on Intelligent Virtual Agents · doi:10.1145/3308532.3329441 · Cited by 136
A survey of multi-agent Human–Robot Interaction systems
Dahiya, A., Aroyo, A. M., Dautenhahn, K., & Smith, S. L. (2022) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2022.104335 · Cited by 154
Selten, F., Robeer, M., & Grimmelikhuijsen, S. (2023) · Public Administration Review · doi:10.1111/puar.13602 · Cited by 148
Wright, J. L., Chen, J. Y. C., & Lakhmani, S. G. (2019) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2019.2925717 · Cited by 137
Self-citationAgent Transparency: A Review of Current Theory and Evidence
Bhaskara, A., Skinner, M., & Loft, S. (2020) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2020.2965529 · Cited by 95
“Let me explain!”: exploring the potential of virtual agents in explainable AI interaction design
Weitz, K., Schiller, D., Schlagowski, R., Huber, T., & André, E. (2020) · Journal on Multimodal User Interfaces · doi:10.1007/s12193-020-00332-0 · Cited by 140
Wang, X., & Qiu, X. (2024) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2024.103777 · Cited by 118
Trust and Team Performance in Human–Autonomy Teaming
McNeese, N. J., Demir, M., Chiou, E. K., & Cooke, N. J. (2021) · International Journal of Electronic Commerce · doi:10.1080/10864415.2021.1846854 · Cited by 125
Kim, T., & Song, H. (2021) · Telematics and Informatics · doi:10.1016/j.tele.2021.101595 · Cited by 101
A Review on Human–Machine Trust Evaluation: Human-Centric and Machine-Centric Perspectives
Gebru, B., Zeleke, L., Blankson, D., Nabil, M., Nateghi, S., Homaifar, A., & Tunstel, E. (2022) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2022.3144956 · Cited by 101
Ethics in human–AI teaming: principles and perspectives
Pflanzer, M., Traylor, Z., Lyons, J. B., Dubljević, V., & Nam, C. S. (2022) · AI and Ethics · doi:10.1007/s43681-022-00214-z · Cited by 106
Unveiling trust in AI: the interplay of antecedents, consequences, and cultural dynamics
Dang, Q., & Li, G. (2025) · AI & Society · doi:10.1007/s00146-025-02477-6 · Cited by 135
Individual Differences in Trust in Autonomous Robots: Implications for Transparency
Matthews, G., Lin, J., Panganiban, A. R., & Long, M. D. (2019) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2019.2947592 · Cited by 100
Musick, G., O’Neill, T., Schelble, B. G., McNeese, N. J., & Henke, J. B. (2021) · Computers in Human Behavior · doi:10.1016/j.chb.2021.106852 · Cited by 102
Bao, Y., Gong, W., & Yang, K. (2023) · Systems · doi:10.3390/systems11090442 · Cited by 108
Trust and Distrust of Automated Parking in a Tesla Model X
Tenhundfeld, N. L., de Visser, E. J., Ries, A. J., Finomore, V. S., & Tossell, C. C. (2019) · Human Factors The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720819865412 · Cited by 83
Sebok, A., & Wickens, C. D. (2017) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720816665201 · Cited by 75
Learning From the Slips of Others: Neural Correlates of Trust in Automated Agents
de Visser, E. J., Beatty, P. J., Estepp, J. R., Kohn, S., Abubshait, A., Fedota, J. R., & McDonald, C. G. (2018) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2018.00309 · Cited by 73
Trust and Human-Machine Teaming: A Qualitative Study
Lyons, J. B., Wynne, K. T., Mahoney, S., & Roebke, M. A. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-817636-8.00006-5 · Cited by 79
Kumar, A., McMahon, G., Reid, T., & Jain, N. (2020) · IEEE Control Systems · doi:10.1109/mcs.2020.3019151 · Cited by 58
Benk, M., Kerstan, S., von Wangenheim, F., & Ferrario, A. (2024) · AI & Society · doi:10.1007/s00146-024-02059-y · Cited by 101
Shaping Trust Through Transparent Design: Theoretical and Experimental Guidelines
Lyons, J. B., Sadler, G. G., Koltai, K., Battiste, H., Ho, N., Hoffmann, L. C., Smith, D. E., Johnson, W. W., & Shively, R. J. (2016) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-41959-6_11 · Cited by 78
Human Performance Benefits of The Automation Transparency Design Principle
Skraaning, G., & Jamieson, G. A. (2019) · Human Factors The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720819887252 · Cited by 75
The effects of explanations on automation bias
Vered, M., Livni, T., Howe, P. D. L., Miller, T., & Sonenberg, L. (2023) · Artificial Intelligence · doi:10.1016/j.artint.2023.103952 · Cited by 83
Automation Stages & Levels, 20 Years After
Wickens, C. D. (2017) · Journal of Cognitive Engineering and Decision Making · doi:10.1177/1555343417727438 · Cited by 66
The IMPACT of Agent Transparency on Human Performance
Stowers, K. L., Kasdaglis, N., Rupp, M. A., Newton, O. B., Chen, J. Y. C., & Barnes, M. J. (2020) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2020.2978041 · Cited by 72
Self-citationUsing agent transparency to support situation awareness of the Autonomous Squad Member
Selkowitz, A. R., Lakhmani, S. G., & Chen, J. Y. C. (2017) · Cognitive Systems Research · doi:10.1016/j.cogsys.2017.02.003 · Cited by 62
Self-citationEffects of Uncertainty and Cognitive Load on User Trust in Predictive Decision Making
Zhou, J., Arshad, S. Z., Luo, S., & Chen, F. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-68059-0_2 · Cited by 59
Understanding the Role of Trust in Human-Autonomy Teaming
McNeese, N. J., Demir, M., Chiou, E. K., Cooke, N. J., & Yanikian, G. (2019) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2019.032 · Cited by 62
Robot Transparency and Team Orientation Effects on Human–Robot Teaming
Guznov, S. Y., Lyons, J. B., Pfahler, M. D., Heironimus, A., Woolley, M., Friedman, J., & Neimeier, A. (2019) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2019.1676519 · Cited by 64
Akash, K., Polson, K., Reid, T., & Jain, N. (2019) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2019.01.028 · Cited by 56
Gaczek, P., Leszczyński, G., & Mouakher, A. (2023) · Industrial Marketing Management · doi:10.1016/j.indmarman.2023.09.007 · Cited by 68
Calibrating Trust in Automation Through Familiarity With the Autoparking Feature of a Tesla Model X
Tenhundfeld, N. L., de Visser, E. J., Haring, K. S., Ries, A. J., Finomore, V. S., & Tossell, C. C. (2019) · Journal of Cognitive Engineering and Decision Making · doi:10.1177/1555343419869083 · Cited by 55
Effect of automation transparency in the management of multiple unmanned vehicles
Bhaskara, A., Duong, L., Brooks, J., Li, R., McInerney, R., Skinner, M., Pongracic, H., & Loft, S. D. (2020) · Applied Ergonomics · doi:10.1016/j.apergo.2020.103243 · Cited by 55
Chen, J., Mishler, S., Hu, B., Li, N., & Proctor, R. W. (2018) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2018.05.010 · Cited by 50
Self-citationArtificial Intelligence and Groups: Effects of Attitudes and Discretion on Collaboration
Bezrukova, K., Griffith, T. L., Spell, C., Rice, V., & Yang, H. E. (2023) · Group & Organization Management · doi:10.1177/10596011231160574 · Cited by 51
Human-Swarm-Teaming Transparency and Trust Architecture
Hepworth, A., Baxter, D. P., Hussein, A., Yaxley, K. J., Debie, E., & Abbass, H. A. (2020) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2020.1003545 · Cited by 57
Zouinar, M. (2020) · Activites · doi:10.4000/activites.4941 · Cited by 85
(Over)Trusting AI Recommendations: How System and Person Variables Affect Dimensions of Complacency
Harbarth, L., Gößwein, E., Bodemer, D., & Schnaubert, L. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2023.2301250 · Cited by 57
Trusting Autonomous Security Robots: The Role of Reliability and Stated Social Intent
Lyons, J. B., Vo, T. Q., Wynne, K. T., Mahoney, S., Nam, C. S., & Gallimore, D. M. (2020) · Human Factors The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720820901629 · Cited by 56
Transparency: Transitioning From Human–Machine Systems to Human-Swarm Systems
Roundtree, K. A., Goodrich, M. A., & Adams, J. A. (2019) · Journal of Cognitive Engineering and Decision Making · doi:10.1177/1555343419842776 · Cited by 49
Egoistic and altruistic motivation: How to induce users’ willingness to help for imperfect AI
Lee, Y., Ha, M., Kwon, S., Shim, Y., & Kim, J. (2019) · Computers in Human Behavior · doi:10.1016/j.chb.2019.06.009 · Cited by 53
Daronnat, S., Azzopardi, L., Halvey, M., & Dubiel, M. (2021) · Frontiers in Robotics and AI · doi:10.3389/frobt.2021.642201 · Cited by 45
Hussein, A., Elsawah, S., & Abbass, H. A. (2020) · Ergonomics · doi:10.1080/00140139.2020.1764112 · Cited by 51
Transparency in Autonomous Teammates
Panganiban, A. R., Matthews, G., & Long, M. D. (2019) · Journal of Cognitive Engineering and Decision Making · doi:10.1177/1555343419881563 · Cited by 48
Effects of transparency on pilot trust and agreement in the autonomous constrained flight planner
Sadler, G. G., Battiste, H., Ho, N., Hoffmann, L., Johnson, W. W., Shively, R. J., Lyons, J. B., & Smith, D. E. (2016) · IEEE/AIAA Digital Avionics Systems Conference (DASC) · doi:10.1109/dasc.2016.7777998 · Cited by 49
Akash, K., Reid, T., & Jain, N. (2019) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2019.01.026 · Cited by 43
Is explainable artificial intelligence intrinsically valuable?
Colaner, N. R. (2021) · AI & Society · doi:10.1007/s00146-021-01184-2 · Cited by 43
Investigating the Effects of Perceived Teammate Artificiality on Human Performance and Cognition
Schelble, B. G., Flathmann, C., McNeese, N. J., O’Neill, T., Pak, R., & Namara, M. (2022) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2022.2085191 · Cited by 45
The pursuit of happiness: the power and influence of AI teammate emotion in human-AI teamwork
Mallick, R., Flathmann, C., Lancaster, C., Hauptman, A. I., McNeese, N. J., & Freeman, G. (2023) · Behaviour and Information Technology · doi:10.1080/0144929x.2023.2277909 · Cited by 42
Intelligent Agent Transparency
Stowers, K. L., Kasdaglis, N., Newton, O. B., Lakhmani, S. G., Wohleber, R. W., & Chen, J. Z.-J. (2016) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/1541931213601392 · Cited by 42
Self-citationAutomated UAV tasks for search and surveillance
Kingston, D. B., Rasmussen, S., & Humphrey, L. R. (2016) · The proceedings of the IEEE Conference on Control Applications/The proceedings of the ... IEEE Conference on Control Applications · doi:10.1109/cca.2016.7587813 · Cited by 41
Thellman, S., & Ziemke, T. (2021) · ACM Transactions on Human-Robot Interaction · doi:10.1145/3461781 · Cited by 45
Taylor, S., Wang, M., & Jeon, M. (2023) · Frontiers in Psychology · doi:10.3389/fpsyg.2023.1121622 · Cited by 40
Impact of Transparency and Explanations on Trust and Situation Awareness in Human–Robot Teams
Ezenyilimba, A., Wong, M. E., Hehr, A. J., Demir, M., Wolff, A. T., Chiou, E. K., & Cooke, N. J. (2022) · Journal of Cognitive Engineering and Decision Making · doi:10.1177/15553434221136358 · Cited by 39
Schodde, T., Hoffmann, L., Stange, S., & Kopp, S. (2019) · ACM Transactions on Human-Robot Interaction · doi:10.1145/3366422 · Cited by 41
XFlag: Explainable Fake News Detection Model on Social Media
Chien, S.-Y. J., Yang, C., & Yu, F. (2022) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2022.2062113 · Cited by 42
Towards Meaningfully Integrating Human-Autonomy Teaming in Applied Settings
Schelble, B. G., Flathmann, C., & Mcneese, N. J. (2020) · International Conference on Human-Agent Interaction · doi:10.1145/3406499.3415077 · Cited by 37
The Effects of Agent Transparency on Human Interaction with an Autonomous Robotic Agent
Selkowitz, A. R., Lakhmani, S. G., Chen, J. Y. C., & Boyce, M. W. (2015) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/1541931215591246 · Cited by 34
Self-citationChien, S.-Y. J., Lin, Y., Lee, P.-J., Han, S., Lewis, M., & Sycara, K. P. (2018) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2018.03.005 · Cited by 35
Evaluation of Playbook Delegation Approach in Human-Autonomy Teaming for Single Pilot Operations
Tokadlı, G., Dorneich, M. C., & Matessa, M. (2021) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2021.1890485 · Cited by 33
Effects of Agent Transparency on human-autonomy teaming effectiveness
Chen, J. Y. C., Barnes, M., Selkowitz, A. R., & Stowers, K. (2016) · IEEE International Conference on Systems, Man and Cybernetics · doi:10.1109/smc.2016.7844505 · Cited by 34
Self-citationExplaining the News Feed Algorithm
Cotter, K., Cho, J., & Rader, E. J. (2017) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3027063.3053114 · Cited by 49
Brokerbot: A Cryptocurrency Chatbot in the Social-technical Gap of Trust
Lee, M., Frank, L. E., & IJsselsteijn, W. A. (2021) · Computer Supported Cooperative Work (CSCW) · doi:10.1007/s10606-021-09392-6 · Cited by 36
Bobko, P., Hirshfield, L., Eloy, L., Spencer, C., Doherty, E., Driscoll, J., & Obolsky, H. (2023) · Theoretical Issues in Ergonomics Science · doi:10.1080/1463922x.2022.2086644 · Cited by 33
Human-Autonomy Teaming and Agent Transparency
Chen, J. Y. C., Barnes, M. J., Selkowitz, A. R., Stowers, K. L., Lakhmani, S. G., & Kasdaglis, N. (2016) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/2876456.2879479 · Cited by 33
Self-citationEffects of Proactive Explanations by Robots on Human-Robot Trust
Zhu, L., & Williams, T. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-62056-1_8 · Cited by 33
Holder, E. W., & Wang, N. (2021) · Human-Intelligent Systems Integration · doi:10.1007/s42454-020-00021-z · Cited by 32
Exploring system wide trust prevalence and mitigation strategies with multiple autonomous agents
Walliser, J. C., de Visser, E. J., & Shaw, T. H. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2023.107671 · Cited by 37
Certifiable Trust in Autonomous Systems: Making the Intractable Tangible
Lyons, J. B., Clark, M. A., Wagner, A. R., & Schuelke, M. J. (2017) · AI Magazine · doi:10.1609/aimag.v38i3.2717 · Cited by 32
Human–Agent Team Dynamics: A Review and Future Research Opportunities
Iftikhar, R., Chiu, Y.-T., Khan, M. S., & Caudwell, C. B. (2023) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2023.3331369 · Cited by 42
Zhang, R., Flathmann, C., Musick, G., Schelble, B. G., McNeese, N. J., Knijnenburg, B. P., & Duan, W. (2023) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3635474 · Cited by 36
Explainable Autonomy: A Study of Explanation Styles for Building Clear Mental Models
Garcia, F. J. C., Robb, D. A., Liu, X., Laskov, A., Patrón, P., & Hastie, H. W. (2018) · International Conference on Natural Language Generation · doi:10.18653/v1/w18-6511 · Cited by 32
Deep learning, transparency, and trust in human robot teamwork
Lewis, M., Li, H.-A., & Sycara, K. (2021) · Trust in Human-Robot Interaction · doi:10.1016/b978-0-12-819472-0.00014-9 · Cited by 29
A Bayesian Trust Inference Model for Human-Multi-Robot Teams
Mahani, M. F., Jiang, L., & Wang, Y. (2020) · International Journal of Social Robotics · doi:10.1007/s12369-020-00705-1 · Cited by 30
A Review of Transparency (seeing-into) Models
Rajabiyazdi, F., & Jamieson, G. A. (2020) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc42975.2020.9282970 · Cited by 29
Metadata
- Authors
- Mercado, J. E., Rupp, M. A., Chen, J. Y. C., Barnes, M. J., Barber, D., & Procci, K.
- Affiliations
- University of Central Florida (US)
- Corresponding author
- Mercado, Joseph E.
- Venue
- Human Factors: The Journal of the Human Factors and Ergonomics Society
- Publisher
- SAGE Publications
- Published
- 2016-02-11
- Volume
- 58
- Issue
- 3
- Pages
- 401-415
- ISSN
- 0018-7208 (ISSN-L)
- 1547-8181
- OpenAlex
- W2321972046
- Semantic Scholar
- 5161e7babcca03e87642f3a130c2d25e338bde9a
- Cited by
- 403
- References
- 44
- Citations by year
- 2026: 32 · 2025: 55 · 2024: 46 · 2023: 47 · 2022: 44 · 2021: 41 · 2020: 45 · 2019: 37 · 2018: 25 · 2017: 16 · 2016: 14
- FWCI
- 48.15
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Human Factors The Journal of the Human Factors and Ergonomics Society (publishedVersion, public-domain)
- PubMed (publishedVersion)
- Journal of International Crisis and Risk Communication Research (submittedVersion)
- Topics
- Social Sciences › Human Factors and Ergonomics › Human-Automation Interaction and Safety
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Physical Sciences › Engineering › Control and Systems Engineering › Military Strategy and Technology
- Keywords
- agent transparency, human-agent teaming, unmanned vehicles, intelligent agents, trust in automation, operator workload, operator performance, usability, military operations, heterogeneous robot teams, trust calibration
- MeSH terms
- Adult, Artificial Intelligence, Automation, Computer Simulation, Female, Humans, Male, Man-Machine Systems, Task Performance and Analysis, User-Computer Interface, Young Adult
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
