AI Systems Trustworthiness Assessment: State of the Art - IRT SystemX Access content directly
Conference Papers Year : 2024

AI Systems Trustworthiness Assessment: State of the Art

Afef Awadid
  • Function : Author
  • PersonId : 1048939
Kahina Amokrane-Ferka
Henri Sohier
  • Function : Author
  • PersonId : 1054948
Juliette Mattioli
Faouzi Adjed
Martin Gonzalez
Souhaiel Khalfaoui
  • Function : Author
  • PersonId : 1337042

Abstract

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.
Fichier principal
Vignette du fichier
MBSE-AI Integration_2024_7_CR.pdf (1.29 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-04400795 , version 1 (17-01-2024)

Identifiers

  • HAL Id : hal-04400795 , version 1

Cite

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⟩
108 View
67 Download

Share

Gmail Facebook X LinkedIn More