@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",
year = "2024",
doi = "10.1007/978-3-031-76428-8\_27",
language = "English",
isbn = "9783031764271",
series = "Springer Proceedings in Advanced Robotics",
publisher = "Springer Nature",
pages = "142--146",
editor = "Cristian Secchi and Lorenzo Marconi",
booktitle = "European Robotics Forum 2024 - 15th ERF",
address = "United States",
}