When Robots Say No: Temporal Trust Recovery Through Explanation

Nicola Webb; Zijun Huang; Sanja Milivojevic; Christopher Baber; Edmund R Hunt

doi:10.1007/978-981-95-2379-5_29

When Robots Say No: Temporal Trust Recovery Through Explanation

Nicola Webb^*
, Zijun Huang
, Sanja Milivojevic
, Christopher Baber
, Edmund R Hunt^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

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Abstract

Mobile robots with some degree of autonomy could deliver significant advantages in high-risk missions such as search and rescue and firefighting. Integrated into a human-robot team (HRT), robots could work effectively to help search hazardous buildings. User trust is a key enabler for HRT, but during a mission, trust can be damaged. With distributed situation awareness, such as when team members are working in different locations, users may be inclined to doubt a robot's integrity if it declines to immediately change its priorities on request. In this paper, we present the results of a computer-based study investigating on-mission trust dynamics in a high-stakes human-robot teaming scenario. Participants (n = 38) played an interactive firefighting game alongside a robot teammate, where a trust violation occurs owing to the robot declining to help the user immediately. We find that when the robot provides an explanation for declining to help, trust better recovers over time, albeit following an initial drop that is comparable to a baseline condition where an explanation for refusal is not provided. Our findings indicate that trust can vary significantly during a mission, notably when robots do not immediately respond to user requests, but that this trust violation can be largely ameliorated over time if adequate explanation is provided.

Original language	English
Title of host publication	Social Robotics + AI
Subtitle of host publication	17th International Conference, ICSR + AI 2025, Naples, Italy, September 10–12, 2025, Proceedings, Part I
Editors	Mariacarla Staffa, John-John Cabibihan, Bruno Siciliano, Shuzhi Sam Ge, Leon Bodenhagen, Adriana Tapus, Silvia Rossi, Filippo Cavallo, Laura Fiorini, Marco Matarese, Hongsheng He
Publisher	Springer Singapore
Pages	424-436
Number of pages	13
ISBN (Electronic)	9789819523795
ISBN (Print)	9789819523788
DOIs	https://doi.org/10.1007/978-981-95-2379-5_29
Publication status	Published - 2 Jan 2026
Event	The International Conference on Social Robotics + AI : 2025 - Naples, Italy Duration: 10 Sept 2025 → 12 Sept 2025 https://icsr2025.eu/

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	1631
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	The International Conference on Social Robotics + AI
Abbreviated title	ICSR + AI 2025
Country/Territory	Italy
City	Naples
Period	10/09/25 → 12/09/25
Internet address	https://icsr2025.eu/

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.

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10.1007/978-981-95-2379-5_29

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Accepted author manuscript, 12.8 MBLicence: CC BY

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Webb, N., Huang, Z., Milivojevic, S., Baber, C., & Hunt, E. R. (2026). When Robots Say No: Temporal Trust Recovery Through Explanation. In M. Staffa, J.-J. Cabibihan, B. Siciliano, S. S. Ge, L. Bodenhagen, A. Tapus, S. Rossi, F. Cavallo, L. Fiorini, M. Matarese, & H. He (Eds.), Social Robotics + AI : 17th International Conference, ICSR + AI 2025, Naples, Italy, September 10–12, 2025, Proceedings, Part I (pp. 424-436). (Lecture Notes in Computer Science; Vol. 1631). Springer Singapore. https://doi.org/10.1007/978-981-95-2379-5_29

Webb, Nicola ; Huang, Zijun ; Milivojevic, Sanja et al. / When Robots Say No : Temporal Trust Recovery Through Explanation. Social Robotics + AI : 17th International Conference, ICSR + AI 2025, Naples, Italy, September 10–12, 2025, Proceedings, Part I. editor / Mariacarla Staffa ; John-John Cabibihan ; Bruno Siciliano ; Shuzhi Sam Ge ; Leon Bodenhagen ; Adriana Tapus ; Silvia Rossi ; Filippo Cavallo ; Laura Fiorini ; Marco Matarese ; Hongsheng He. Springer Singapore, 2026. pp. 424-436 (Lecture Notes in Computer Science).

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title = "When Robots Say No: Temporal Trust Recovery Through Explanation",

abstract = "Mobile robots with some degree of autonomy could deliver significant advantages in high-risk missions such as search and rescue and firefighting. Integrated into a human-robot team (HRT), robots could work effectively to help search hazardous buildings. User trust is a key enabler for HRT, but during a mission, trust can be damaged. With distributed situation awareness, such as when team members are working in different locations, users may be inclined to doubt a robot's integrity if it declines to immediately change its priorities on request. In this paper, we present the results of a computer-based study investigating on-mission trust dynamics in a high-stakes human-robot teaming scenario. Participants (n = 38) played an interactive firefighting game alongside a robot teammate, where a trust violation occurs owing to the robot declining to help the user immediately. We find that when the robot provides an explanation for declining to help, trust better recovers over time, albeit following an initial drop that is comparable to a baseline condition where an explanation for refusal is not provided. Our findings indicate that trust can vary significantly during a mission, notably when robots do not immediately respond to user requests, but that this trust violation can be largely ameliorated over time if adequate explanation is provided. ",

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note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.; The International Conference on Social Robotics + AI : 2025, ICSR + AI 2025 ; Conference date: 10-09-2025 Through 12-09-2025",

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editor = "Mariacarla Staffa and John-John Cabibihan and Bruno Siciliano and Ge, \{Shuzhi Sam\} and Leon Bodenhagen and Adriana Tapus and Silvia Rossi and Filippo Cavallo and Laura Fiorini and Marco Matarese and Hongsheng He",

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Webb, N, Huang, Z, Milivojevic, S, Baber, C & Hunt, ER 2026, When Robots Say No: Temporal Trust Recovery Through Explanation. in M Staffa, J-J Cabibihan, B Siciliano, SS Ge, L Bodenhagen, A Tapus, S Rossi, F Cavallo, L Fiorini, M Matarese & H He (eds), Social Robotics + AI : 17th International Conference, ICSR + AI 2025, Naples, Italy, September 10–12, 2025, Proceedings, Part I. Lecture Notes in Computer Science, vol. 1631, Springer Singapore, pp. 424-436, The International Conference on Social Robotics + AI , Naples, Italy, 10/09/25. https://doi.org/10.1007/978-981-95-2379-5_29

When Robots Say No: Temporal Trust Recovery Through Explanation. / Webb, Nicola; Huang, Zijun; Milivojevic, Sanja et al.
Social Robotics + AI : 17th International Conference, ICSR + AI 2025, Naples, Italy, September 10–12, 2025, Proceedings, Part I. ed. / Mariacarla Staffa; John-John Cabibihan; Bruno Siciliano; Shuzhi Sam Ge; Leon Bodenhagen; Adriana Tapus; Silvia Rossi; Filippo Cavallo; Laura Fiorini; Marco Matarese; Hongsheng He. Springer Singapore, 2026. p. 424-436 (Lecture Notes in Computer Science; Vol. 1631).

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

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T1 - When Robots Say No

T2 - The International Conference on Social Robotics + AI

AU - Webb, Nicola

AU - Huang, Zijun

AU - Milivojevic, Sanja

AU - Baber, Christopher

AU - Hunt, Edmund R

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.

PY - 2026/1/2

Y1 - 2026/1/2

N2 - Mobile robots with some degree of autonomy could deliver significant advantages in high-risk missions such as search and rescue and firefighting. Integrated into a human-robot team (HRT), robots could work effectively to help search hazardous buildings. User trust is a key enabler for HRT, but during a mission, trust can be damaged. With distributed situation awareness, such as when team members are working in different locations, users may be inclined to doubt a robot's integrity if it declines to immediately change its priorities on request. In this paper, we present the results of a computer-based study investigating on-mission trust dynamics in a high-stakes human-robot teaming scenario. Participants (n = 38) played an interactive firefighting game alongside a robot teammate, where a trust violation occurs owing to the robot declining to help the user immediately. We find that when the robot provides an explanation for declining to help, trust better recovers over time, albeit following an initial drop that is comparable to a baseline condition where an explanation for refusal is not provided. Our findings indicate that trust can vary significantly during a mission, notably when robots do not immediately respond to user requests, but that this trust violation can be largely ameliorated over time if adequate explanation is provided.

AB - Mobile robots with some degree of autonomy could deliver significant advantages in high-risk missions such as search and rescue and firefighting. Integrated into a human-robot team (HRT), robots could work effectively to help search hazardous buildings. User trust is a key enabler for HRT, but during a mission, trust can be damaged. With distributed situation awareness, such as when team members are working in different locations, users may be inclined to doubt a robot's integrity if it declines to immediately change its priorities on request. In this paper, we present the results of a computer-based study investigating on-mission trust dynamics in a high-stakes human-robot teaming scenario. Participants (n = 38) played an interactive firefighting game alongside a robot teammate, where a trust violation occurs owing to the robot declining to help the user immediately. We find that when the robot provides an explanation for declining to help, trust better recovers over time, albeit following an initial drop that is comparable to a baseline condition where an explanation for refusal is not provided. Our findings indicate that trust can vary significantly during a mission, notably when robots do not immediately respond to user requests, but that this trust violation can be largely ameliorated over time if adequate explanation is provided.

U2 - 10.1007/978-981-95-2379-5_29

DO - 10.1007/978-981-95-2379-5_29

M3 - Conference Contribution (Conference Proceeding)

SN - 9789819523788

T3 - Lecture Notes in Computer Science

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A2 - Fiorini, Laura

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PB - Springer Singapore

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Webb N, Huang Z, Milivojevic S, Baber C, Hunt ER. When Robots Say No: Temporal Trust Recovery Through Explanation. In Staffa M, Cabibihan JJ, Siciliano B, Ge SS, Bodenhagen L, Tapus A, Rossi S, Cavallo F, Fiorini L, Matarese M, He H, editors, Social Robotics + AI : 17th International Conference, ICSR + AI 2025, Naples, Italy, September 10–12, 2025, Proceedings, Part I. Springer Singapore. 2026. p. 424-436. (Lecture Notes in Computer Science). doi: 10.1007/978-981-95-2379-5_29

When Robots Say No: Temporal Trust Recovery Through Explanation

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