Explanation Styles for Trustworthy Autonomous Systems

David A. Robb; Xingkun Liu; Helen Hastie

doi:10.5555/3545946.3598913

Explanation Styles for Trustworthy Autonomous Systems

David A. Robb, Xingkun Liu, Helen Hastie

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Citations (Scopus)

129 Downloads (Pure)

Abstract

We present a study that explores the formulation of natural language explanations for managing the appropriate amount of trust in a remote autonomous system that fails to complete its mission. Online crowd-sourced participants were shown video vignettes of robots performing an inspection task. We measured participants' mental models, their confidence in their understanding of the robot behaviour and their trust in the robot. We found that including history in the explanation increases trust and confidence, and helps maintain an accurate mental model, but only if context is also included. In addition, our study exposes that some explanation formulations lacking in context can lead to misplaced participant confidence.

Original language	English
Title of host publication	Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems (AAMAS '23)
Publisher	Association for Computing Machinery
Pages	2298–2300
Number of pages	3
ISBN (Print)	9781450394321
DOIs	https://doi.org/10.5555/3545946.3598913
Publication status	Published - 30 May 2023
Event	22nd International Conference on Autonomous Agents and Multiagent Systems 2023 - London ExCeL conference centre, London, United Kingdom Duration: 29 May 2023 → 2 Jun 2023 https://aamas2023.soton.ac.uk/

Conference

Conference	22nd International Conference on Autonomous Agents and Multiagent Systems 2023
Abbreviated title	AAMAS 2023
Country/Territory	United Kingdom
City	London
Period	29/05/23 → 2/06/23
Internet address	https://aamas2023.soton.ac.uk/

Keywords

Explanations
transparency
trust
robot faults
mental models

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10.5555/3545946.3598913

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Cite this

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title = "Explanation Styles for Trustworthy Autonomous Systems",

abstract = "We present a study that explores the formulation of natural language explanations for managing the appropriate amount of trust in a remote autonomous system that fails to complete its mission. Online crowd-sourced participants were shown video vignettes of robots performing an inspection task. We measured participants' mental models, their confidence in their understanding of the robot behaviour and their trust in the robot. We found that including history in the explanation increases trust and confidence, and helps maintain an accurate mental model, but only if context is also included. In addition, our study exposes that some explanation formulations lacking in context can lead to misplaced participant confidence.",

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}

Robb, DA, Liu, X & Hastie, H 2023, Explanation Styles for Trustworthy Autonomous Systems. in Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems (AAMAS '23). Association for Computing Machinery, pp. 2298–2300, 22nd International Conference on Autonomous Agents and Multiagent Systems 2023, London, United Kingdom, 29/05/23. https://doi.org/10.5555/3545946.3598913

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AU - Liu, Xingkun

AU - Hastie, Helen

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N2 - We present a study that explores the formulation of natural language explanations for managing the appropriate amount of trust in a remote autonomous system that fails to complete its mission. Online crowd-sourced participants were shown video vignettes of robots performing an inspection task. We measured participants' mental models, their confidence in their understanding of the robot behaviour and their trust in the robot. We found that including history in the explanation increases trust and confidence, and helps maintain an accurate mental model, but only if context is also included. In addition, our study exposes that some explanation formulations lacking in context can lead to misplaced participant confidence.

AB - We present a study that explores the formulation of natural language explanations for managing the appropriate amount of trust in a remote autonomous system that fails to complete its mission. Online crowd-sourced participants were shown video vignettes of robots performing an inspection task. We measured participants' mental models, their confidence in their understanding of the robot behaviour and their trust in the robot. We found that including history in the explanation increases trust and confidence, and helps maintain an accurate mental model, but only if context is also included. In addition, our study exposes that some explanation formulations lacking in context can lead to misplaced participant confidence.

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KW - transparency

KW - trust

KW - robot faults

KW - mental models

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BT - Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems (AAMAS '23)

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T2 - 22nd International Conference on Autonomous Agents and Multiagent Systems 2023

Y2 - 29 May 2023 through 2 June 2023

ER -

Explanation Styles for Trustworthy Autonomous Systems

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