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Trustworthiness assurance assessment for high-risk AI-based systems
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Stettinger, Georg, Weissensteiner, Patrick and Khastgir, Siddartha (2024) Trustworthiness assurance assessment for high-risk AI-based systems. IEEE Access, 12 . pp. 22718-22745. doi:10.1109/access.2024.3364387 ISSN 2169-3536.
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Official URL: http://doi.org/10.1109/access.2024.3364387
Abstract
This work proposes methodologies for ensuring the trustworthiness of high-risk artificial
intelligence (AI) systems (AIS) to achieve compliance with the European Union’s (EU) AI Act. Highrisk classified AIS must fulfill seven requirements to be considered trustworthy and human-centric, and
subsequently be considered for deployment. These requirements are equally important, mutually supportive,
and should be implemented and evaluated throughout the AI lifecycle. The assurance of trustworthiness is
influenced by ethical considerations, amongst others. Hence, the operational design domain (ODD) and
behavior competency (BC) concepts from the automated driving domain are utilized in risk assessment
strategies to quantify different types of residual risks. The methodology presented is guided by the consistent
application of the ODD and its related BC concept throughout the entire AI lifecycle, focusing on the
trustworthiness assurance framework and its associated process as the main pillars for AIS certification.
The achievement of the overall objective of trustworthy and human-centric AIS is divided into seven
interconnected sub-goals: the formulation of use restrictions, the trustworthiness assurance/argument itself,
the identification of dysfunctional cases, the utilization of scenario This work proposes methodologies for ensuring the trustworthiness of high-risk artificial intelligence (AI) systems (AIS) to achieve compliance with the European Union’s (EU) AI Act. High-risk classified AIS must fulfill seven requirements to be considered trustworthy and human-centric, and subsequently be considered for deployment. These requirements are equally important, mutually supportive, and should be implemented and evaluated throughout the AI lifecycle. The assurance of trustworthiness is influenced by ethical considerations, amongst others. Hence, the operational design domain (ODD) and behavior competency (BC) concepts from the automated driving domain are utilized in risk assessment strategies to quantify different types of residual risks. The methodology presented is guided by the consistent application of the ODD and its related BC concept throughout the entire AI lifecycle, focusing on the trustworthiness assurance framework and its associated process as the main pillars for AIS certification. The achievement of the overall objective of trustworthy and human-centric AIS is divided into seven interconnected sub-goals: the formulation of use restrictions, the trustworthiness assurance/argument itself, the identification of dysfunctional cases, the utilization of scenario databases and datasets, the application of metrics for evaluation, the implementation of the proposed concept across the AI lifecycle, and sufficient consideration of human factors. The role of standards in the assurance process is discussed, considering any existing gaps and areas for improvement. The work concludes with a summary of the developed approach, highlighting key takeaways and action points. Finally, a roadmap to ensure trustworthy and human-centric behavior of future AIS is outlined.
Item Type: | Journal Article | ||||
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | ||||
SWORD Depositor: | Library Publications Router | ||||
Journal or Publication Title: | IEEE Access | ||||
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) | ||||
ISSN: | 2169-3536 | ||||
Official Date: | 8 February 2024 | ||||
Dates: |
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Volume: | 12 | ||||
Page Range: | pp. 22718-22745 | ||||
DOI: | 10.1109/access.2024.3364387 | ||||
Status: | Peer Reviewed | ||||
Publication Status: | Published | ||||
Access rights to Published version: | Open Access (Creative Commons) | ||||
Date of first compliant deposit: | 19 March 2024 | ||||
Date of first compliant Open Access: | 19 March 2024 | ||||
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