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Developing and Operating Artificial Intelligence Models in Trustworthy Autonomous Systems

 
: Martínez-Fernández, Silverio; Franch, Xavier; Jedlitschka, Andreas; Oriol, Marc; Trendowicz, Adam

:

Cherfi, S.:
Research Challenges in Information Science, 15th International Conference, RCIS 2021. Proceedings : Limassol, Cyprus, 11th - 14th May 2021
Cham: Springer International Publishing, 2021 (Lecture Notes in Business Information Processing 415)
ISBN: 978-3-030-75018-3
ISBN: 978-3-030-75017-6
ISBN: 978-3-030-75019-0
S.221-229
International Conference on Research Challenges in Information Science (RCIS) <15, 2021, Online>
Englisch
Konferenzbeitrag
Fraunhofer IESE ()
DevOps ; Autonomous Systems ; AI ; Trustworthiness

Abstract
Companies dealing with Artificial Intelligence (AI) models in Autonomous Systems (AS) face several problems, such as users’ lack of trust in adverse or unknown conditions, gaps between software engineering and AI model development, and operation in a continuously changing operational environment. This work-in-progress paper aims to close the gap between the development and operation of trustworthy AI-based AS by defining an approach that coordinates both activities. We synthesize the main challenges of AI-based AS in industrial settings. We reflect on the research efforts required to overcome these challenges and propose a novel, holistic DevOps approach to put it into practice. We elaborate on four research directions: (a) increased users’ trust by monitoring operational AI-based AS and identifying self-adaptation needs in critical situations; (b) integrated agile process for the development and evolution of AI models and AS; (c) continuous deployment of different context-specific instances of AI models in a distributed setting of AS; and (d) holistic DevOps-based lifecycle for AI-based AS.

: http://publica.fraunhofer.de/dokumente/N-640345.html