Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

When Robots Say No: Temporal Trust Recovery Through Explanation

Nicola Webb, Zijun Huang, Sanja Milivojevic, Chris Baber, Edmund R. Hunt

TL;DR

This paper investigates how trust in a robotic teammate evolves during high-stakes missions when the robot declines a user request and whether a provided explanation can repair trust. Using a computer-based firefighting game with 38 participants assigned to baseline-refusal or explanation conditions, the study captures on-mission trust dynamics via nag prompts and post-mission surveys. Results show an initial trust dip at the violation in both conditions, but trust recovers more fully and quickly when an explanation is given, with significant between-condition differences by the end. The findings support explanation-based trust repair as a practical mechanism to sustain HRT performance in distributed, time-sensitive scenarios.

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.

When Robots Say No: Temporal Trust Recovery Through Explanation

TL;DR

This paper investigates how trust in a robotic teammate evolves during high-stakes missions when the robot declines a user request and whether a provided explanation can repair trust. Using a computer-based firefighting game with 38 participants assigned to baseline-refusal or explanation conditions, the study captures on-mission trust dynamics via nag prompts and post-mission surveys. Results show an initial trust dip at the violation in both conditions, but trust recovers more fully and quickly when an explanation is given, with significant between-condition differences by the end. The findings support explanation-based trust repair as a practical mechanism to sustain HRT performance in distributed, time-sensitive scenarios.

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.
Paper Structure (14 sections, 4 figures, 3 tables)

This paper contains 14 sections, 4 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Trust in human-robot teaming
  4. Trust repair strategies
  5. Research questions
  6. Methodology
  7. Firefighting Game Gameplay
  8. Data Collection
  9. Results
  10. Trust score over time
  11. Overall Trust Score
  12. General Attitudes Towards Robots Scale (GAToRS) and Trust Score
  13. Discussion and Future Work
  14. Conclusion

Figures (4)

  • Figure 1: Top: Timeline of gameplay, including both break-point messages. Numbers indicate nag points and the types of fire-related tasks occurring between them. Bottom: Selected gameplay snapshots showing key moments: training session, entering the building, collaborative firefighting, calling the robot, trust break point, additional fires inside, a "Stay back from the flames" warning, and exiting the building.
  • Figure 2: Mean nag scores throughout game in both conditions
  • Figure 3: Individual Schaefer trust survey item changes pre-post interaction
  • Figure 4: Supplementary trust questions, changes pre-post interaction