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Conference Papers Year : 2024

AI Systems Trustworthiness Assessment: State of the Art

Afef Awadid
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Kahina Amokrane-Ferka
Henri Sohier
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Juliette Mattioli
Faouzi Adjed
Martin Gonzalez
Souhaiel Khalfaoui
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Model-based System Engineering (MBSE) has been advocated as a promising approach to reduce the complexity of AI-based systems development. However, given the uncertainties and risks associated with Artificial Intelligence (AI), the successful application of MBSE requires the assessment of AI trustworthiness. To deal with this issue, this paper provides a state of the art review of AI trustworthiness assessment in terms of trustworthiness attributes/ characteristics and their corresponding evaluation metrics. Examples of such attributes include data quality, robustness, and explainability. The proposed review is based on academic and industrial literature conducted within the research program.
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hal-04400795 , version 1 (17-01-2024)


  • HAL Id : hal-04400795 , version 1


Afef Awadid, Kahina Amokrane-Ferka, Henri Sohier, Juliette Mattioli, Faouzi Adjed, et al.. AI Systems Trustworthiness Assessment: State of the Art. Workshop on Model-based System Engineering and Artificial Intelligence - MBSE-AI Integration 2024, Feb 2024, Rome, Italy. ⟨hal-04400795⟩
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