Broken Trust: Does the Agent Matter?

Trust is a key part of any social interaction, whether that be between humans, or humans interacting with different artificial agents. This paper investigates how an agent’s repeated incongruence failure might impact users’ trust. We augment a previously published human-robot interaction study (Nesset et al., 2023), by replacing the robot condition with a human actor. Here, we explore how users’ trust can be impacted by repeated failure depending on the agent involved and how to best repair trust once the failures take place. Our study found a significant decrease in users’ trust when a human makes an incongruence failure, but not when this failure was repeated, regardless of the repair strategy implemented. When comparing this to the previous robot condition, we found a significant difference in the trust measured in the human and the robot condition. Additionally, the repair strategy used had a significant effect on the users’ trust when the robot repeated its failure but not when the actor did. Our findings contribute to research on broken trust with repeated failures and highlight the importance of including a human comparison to better understand research findings in human-robot interactions.

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Published
2024-11-20
DOI
https://doi.org/10.1145/3687272.3688307
Primary Topic
Ethics and Social Impacts of AI
Type
article
Field-Weighted Citation Impact
0.00

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article

Broken Trust: Does the Agent Matter?

Gnanathusharan Rajendran, Marta Romeo, Birthe Nesset
Ethics and Social Impacts of AI
article

Broken Trust: Does the Agent Matter?

Gnanathusharan Rajendran, Marta Romeo, Birthe Nesset
article en

Abstract

Trust is a key part of any social interaction, whether that be between humans, or humans interacting with different artificial agents. This paper investigates how an agent’s repeated incongruence failure might impact users’ trust. We augment a previously published human-robot interaction study (Nesset et al., 2023), by replacing the robot condition with a human actor. Here, we explore how users’ trust can be impacted by repeated failure depending on the agent involved and how to best repair trust once the failures take place. Our study found a significant decrease in users’ trust when a human makes an incongruence failure, but not when this failure was repeated, regardless of the repair strategy implemented. When comparing this to the previous robot condition, we found a significant difference in the trust measured in the human and the robot condition. Additionally, the repair strategy used had a significant effect on the users’ trust when the robot repeated its failure but not when the actor did. Our findings contribute to research on broken trust with repeated failures and highlight the importance of including a human comparison to better understand research findings in human-robot interactions.

Heriot-Watt University (GB)
UK Research and Innovation
Peace, Justice and strong institutions
Openalex Percentile: Top 75%
Ethics and Social Impacts of AI
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