Ensuring Trustworthiness of Hybrid AI-Based Robotics Systems

Alexander Eguia; Nuria Quintano; Irina Marsh; Michel Barreteau; Jakub Główka; Agnieszka Sprońska

doi:10.1007/978-3-031-76428-8_27

Ensuring Trustworthiness of Hybrid AI-Based Robotics Systems

Alexander Eguia^*
, Nuria Quintano
, Irina Marsh
, Michel Barreteau
, Jakub Główka
, Agnieszka Sprońska

^*Corresponding author for this work

Cyber defense and industrial cybersecurity

Basque Research and Technology Alliance (BRTA)
CBRNE Ltd
Thales
Industrial Research Institute for Automation and Measurements, Warsaw

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

Abstract

Hybrid Artificial Intelligence (HAI) algorithms are well adapted to the industrial environment since they require significantly less data than data-driven Artificial Intelligence (AI) algorithms, matching the industry constraints. However, HAI does not fully address the issue of trust (validity, transparency, explainability, and ethics) which must be tackled to achieve world-class HAI beneficial to humans individually, organisationally, and societally. This paper focuses on describing the methods used to collect and elicit trustworthiness requirements in the European ULTIMATE project. It includes requirements register and a trustworthiness glossary of terms.

Original language	English
Title of host publication	European Robotics Forum 2024 - 15th ERF
Editors	Cristian Secchi, Lorenzo Marconi
Publisher	Springer Nature
Pages	142-146
Number of pages	5
ISBN (Print)	9783031764271
DOIs	https://doi.org/10.1007/978-3-031-76428-8_27
Publication status	Published - 2024
Event	15th European Robotics Forum, ERF 2024 - Rimini, Italy Duration: 13 Mar 2024 → 15 Mar 2024

Publication series

Name	Springer Proceedings in Advanced Robotics
Volume	33 SPAR
ISSN (Print)	2511-1256
ISSN (Electronic)	2511-1264

Conference

Conference	15th European Robotics Forum, ERF 2024
Country/Territory	Italy
City	Rimini
Period	13/03/24 → 15/03/24

Keywords

ethics
hybrid AI
robotics
trustworthiness

Access to Document

10.1007/978-3-031-76428-8_27

Cite this

@inproceedings{e48c53970f56402ca6e88daeaaa39622,

title = "Ensuring Trustworthiness of Hybrid AI-Based Robotics Systems",

abstract = "Hybrid Artificial Intelligence (HAI) algorithms are well adapted to the industrial environment since they require significantly less data than data-driven Artificial Intelligence (AI) algorithms, matching the industry constraints. However, HAI does not fully address the issue of trust (validity, transparency, explainability, and ethics) which must be tackled to achieve world-class HAI beneficial to humans individually, organisationally, and societally. This paper focuses on describing the methods used to collect and elicit trustworthiness requirements in the European ULTIMATE project. It includes requirements register and a trustworthiness glossary of terms.",

keywords = "ethics, hybrid AI, robotics, trustworthiness",

author = "Alexander Eguia and Nuria Quintano and Irina Marsh and Michel Barreteau and Jakub G{\l}{\'o}wka and Agnieszka Spro{\'n}ska",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.; 15th European Robotics Forum, ERF 2024 ; Conference date: 13-03-2024 Through 15-03-2024",

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doi = "10.1007/978-3-031-76428-8\_27",

language = "English",

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}

Eguia, A, Quintano, N, Marsh, I, Barreteau, M, Główka, J & Sprońska, A 2024, Ensuring Trustworthiness of Hybrid AI-Based Robotics Systems. in C Secchi & L Marconi (eds), European Robotics Forum 2024 - 15th ERF. Springer Proceedings in Advanced Robotics, vol. 33 SPAR, Springer Nature, pp. 142-146, 15th European Robotics Forum, ERF 2024, Rimini, Italy, 13/03/24. https://doi.org/10.1007/978-3-031-76428-8_27

Ensuring Trustworthiness of Hybrid AI-Based Robotics Systems. / Eguia, Alexander; Quintano, Nuria; Marsh, Irina et al.
European Robotics Forum 2024 - 15th ERF. ed. / Cristian Secchi; Lorenzo Marconi. Springer Nature, 2024. p. 142-146 (Springer Proceedings in Advanced Robotics; Vol. 33 SPAR).

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

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AB - Hybrid Artificial Intelligence (HAI) algorithms are well adapted to the industrial environment since they require significantly less data than data-driven Artificial Intelligence (AI) algorithms, matching the industry constraints. However, HAI does not fully address the issue of trust (validity, transparency, explainability, and ethics) which must be tackled to achieve world-class HAI beneficial to humans individually, organisationally, and societally. This paper focuses on describing the methods used to collect and elicit trustworthiness requirements in the European ULTIMATE project. It includes requirements register and a trustworthiness glossary of terms.

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