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Communication Dans Un Congrès Année : 2024

Assurance Cases to face the complexity of ML-based systems verification

Résumé

The verification and validation of AI-based systems raise new issues that are not easily addressed by existing practices and standards. We think that this gap is actually an opportunity to introduce new practices and establish a clearer and more formal link between the engineering activities and artefacts, the expected properties of the system, and the verification and validation evidence. Therefore, in this paper, we describe and illustrate an approach integrating (i) the definition and modelling of an AI-based system engineering workflow, (ii) the identification of the trustworthiness properties, and (iii) the argumentation demonstrating the satisfaction of these properties. This approach is centred on the model of Assurance Cases, a semi-formal representation of argumentation which supports the claim of system trustworthiness. In addition, we present supporting tools for this formalism that enable the automatic production of Verification and Validation plans for specific properties of AI-based systems.
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Dates et versions

hal-04588599 , version 1 (27-05-2024)

Identifiants

  • HAL Id : hal-04588599 , version 1

Citer

Vincent Mussot, Eric Jenn, Florent Chenevier, Ramon Conejo Laguna, Yassir Id Messaoud, et al.. Assurance Cases to face the complexity of ML-based systems verification. Embedded Real Time System Congress, ERTS'24, Jun 2024, Toulouse, France. ⟨hal-04588599⟩
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