AI Systems Trustworthiness Assessment: State of the Art

Afef Awadid (1) , Kahina Amokrane-Ferka (1) , Henri Sohier (1) , Juliette Mattioli (2) , Faouzi Adjed (1) , Martin Gonzalez (1) , Souhaiel Khalfaoui (3)

1 IRT SystemX
2 THALES [France]
3 VALEO

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 Confiance.ai research program.

Keywords: AI-Based Systems; Trustworthiness Assessment; Trustworthiness Attributes; Metrics; State of the Art Review

Domains: Computer Science [cs]

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 AI, 12th International Conference on Model-Based Software and Systems Engineering (Modelsward), Feb 2024, Rome, Italy. ⟨hal-04400795⟩