Towards Trustworthy Autonomous Systems: Taxonomies and Future Perspectives

TitleTowards Trustworthy Autonomous Systems: Taxonomies and Future Perspectives
Publication TypeJournal Article
Year of Publication2022
AuthorsF. Flammini, C. Alcaraz, E. Bellini, S. Marrone, J. Lopez, and A. Bondavalli
JournalIEEE Transactions on Emerging Topics in Computing
ISSN Number2168-6750

The class of Trustworthy Autonomous Systems (TAS) includes cyber-physical systems leveraging on self-x technologies that make them capable to learn, adapt to changes, and reason under uncertainties in possibly critical applications and evolving environments. In the last decade, there has been a growing interest in enabling artificial intelligence technologies, such as advanced machine learning, new threats, such as adversarial attacks, and certification challenges, due to the lack of sufficient explainability. However, in order to be trustworthy, those systems also need to be dependable, secure, and resilient according to well-established taxonomies, methodologies, and tools. Therefore, several aspects need to be addressed for TAS, ranging from proper taxonomic classification to the identification of research opportunities and challenges. Given such a context, in this paper address relevant taxonomies and research perspectives in the field of TAS. We start from basic definitions and move towards future perspectives, regulations, and emerging technologies supporting development and operation of TAS.

Citation KeyFlamini2022