Explainable Autonomy: A Study of Explanation Styles for Building Clear Mental Models

Francisco Javier Chiyah Garcia; David A. Robb; Xingkun Liu; Atanas Laskov; Pedro Patron; Helen Hastie

Explainable Autonomy: A Study of Explanation Styles for Building Clear Mental Models

Francisco Javier Chiyah Garcia
, David A. Robb
, Xingkun Liu
, Atanas Laskov
, Pedro Patron
, Helen Hastie

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

Abstract

As unmanned vehicles become more autonomous, it is important to maintain a high level of transparency regarding their behaviour and how they operate. This is particularly important in remote locations where they cannot be directly observed. Here, we describe a method for generating explanations in natural language of autonomous system behaviour and reasoning. Our method involves deriving an interpretable model of autonomy through having an expert ‘speak aloud’ and providing various levels of detail based on this model. Through an online evaluation study with operators, we show it is best to generate explanations with multiple possible reasons but tersely worded. This work has implications for designing interfaces for autonomy as well as for explainable AI and operator training.

Original language	English
Title of host publication	Proceedings of the 11th International Conference on Natural Language Generation
Publisher	Association for Computational Linguistics
Pages	99-108
Number of pages	10
ISBN (Electronic)	9781948087865
Publication status	Published - 1 Nov 2018
Event	11th International Conference on Natural Language Generation - Tilburg, Netherlands Duration: 5 Nov 2018 → 8 Nov 2018 https://inlg2018.uvt.nl/

Conference

Conference	11th International Conference on Natural Language Generation
Abbreviated title	INLG 2018
Country/Territory	Netherlands
City	Tilburg
Period	5/11/18 → 8/11/18
Internet address	https://inlg2018.uvt.nl/

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Explainable Autonomy_GARCIA_DOA07092018_VOR_CC BYFinal published version, 1.39 MBLicence: Creative Commons: Attribution (CC-BY)

UK Robotics and Artificial Intelligence Hub for Offshore Energy Asset Integrity Management (ORCA)
Vijayakumar, S. (Principal Investigator), Mistry, M. (Co-investigator), Ramamoorthy, R. (Co-investigator) & Williams, C. (Co-investigator)
EPSRC
1/10/17 → 31/03/22
Project: Research

Cite this

@inproceedings{2bc7d6e9a9724af5b662aca3f0f91f7f,

title = "Explainable Autonomy: A Study of Explanation Styles for Building Clear Mental Models",

abstract = "As unmanned vehicles become more autonomous, it is important to maintain a high level of transparency regarding their behaviour and how they operate. This is particularly important in remote locations where they cannot be directly observed. Here, we describe a method for generating explanations in natural language of autonomous system behaviour and reasoning. Our method involves deriving an interpretable model of autonomy through having an expert {\textquoteleft}speak aloud{\textquoteright} and providing various levels of detail based on this model. Through an online evaluation study with operators, we show it is best to generate explanations with multiple possible reasons but tersely worded. This work has implications for designing interfaces for autonomy as well as for explainable AI and operator training.",

author = "Garcia, \{Francisco Javier Chiyah\} and Robb, \{David A.\} and Xingkun Liu and Atanas Laskov and Pedro Patron and Helen Hastie",

note = "This research was funded by EPSRC ORCAH ub (EP/R026173/1, 2017-2021); RAEng/ Leverhulme Trust Senior Research Fellowship Scheme (Hastie/ LTSRF1617/13/37).; 11th International Conference on Natural Language Generation , INLG 2018 ; Conference date: 05-11-2018 Through 08-11-2018",

year = "2018",

month = nov,

day = "1",

language = "English",

pages = "99--108",

booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",

publisher = "Association for Computational Linguistics",

url = "https://inlg2018.uvt.nl/",

}

Garcia, FJC, Robb, DA, Liu, X, Laskov, A, Patron, P & Hastie, H 2018, Explainable Autonomy: A Study of Explanation Styles for Building Clear Mental Models. in Proceedings of the 11th International Conference on Natural Language Generation. Association for Computational Linguistics, pp. 99-108, 11th International Conference on Natural Language Generation , Tilburg, Netherlands, 5/11/18. <http://10.18653/v1/W18-6511>

Explainable Autonomy: A Study of Explanation Styles for Building Clear Mental Models. / Garcia, Francisco Javier Chiyah; Robb, David A.; Liu, Xingkun et al.
Proceedings of the 11th International Conference on Natural Language Generation. Association for Computational Linguistics, 2018. p. 99-108.

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

TY - GEN

T1 - Explainable Autonomy: A Study of Explanation Styles for Building Clear Mental Models

AU - Garcia, Francisco Javier Chiyah

AU - Robb, David A.

AU - Liu, Xingkun

AU - Laskov, Atanas

AU - Patron, Pedro

AU - Hastie, Helen

N1 - This research was funded by EPSRC ORCAH ub (EP/R026173/1, 2017-2021); RAEng/ Leverhulme Trust Senior Research Fellowship Scheme (Hastie/ LTSRF1617/13/37).

PY - 2018/11/1

Y1 - 2018/11/1

N2 - As unmanned vehicles become more autonomous, it is important to maintain a high level of transparency regarding their behaviour and how they operate. This is particularly important in remote locations where they cannot be directly observed. Here, we describe a method for generating explanations in natural language of autonomous system behaviour and reasoning. Our method involves deriving an interpretable model of autonomy through having an expert ‘speak aloud’ and providing various levels of detail based on this model. Through an online evaluation study with operators, we show it is best to generate explanations with multiple possible reasons but tersely worded. This work has implications for designing interfaces for autonomy as well as for explainable AI and operator training.

AB - As unmanned vehicles become more autonomous, it is important to maintain a high level of transparency regarding their behaviour and how they operate. This is particularly important in remote locations where they cannot be directly observed. Here, we describe a method for generating explanations in natural language of autonomous system behaviour and reasoning. Our method involves deriving an interpretable model of autonomy through having an expert ‘speak aloud’ and providing various levels of detail based on this model. Through an online evaluation study with operators, we show it is best to generate explanations with multiple possible reasons but tersely worded. This work has implications for designing interfaces for autonomy as well as for explainable AI and operator training.

M3 - Conference contribution

SP - 99

EP - 108

BT - Proceedings of the 11th International Conference on Natural Language Generation

PB - Association for Computational Linguistics

T2 - 11th International Conference on Natural Language Generation

Y2 - 5 November 2018 through 8 November 2018

ER -

Explainable Autonomy: A Study of Explanation Styles for Building Clear Mental Models

Abstract

Conference

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Projects

UK Robotics and Artificial Intelligence Hub for Offshore Energy Asset Integrity Management (ORCA)

Cite this