Empowering the trustworthiness of ML-based critical systems through engineering activities - IRT SystemX Accéder directement au contenu
Pré-Publication, Document De Travail Année : 2022

Empowering the trustworthiness of ML-based critical systems through engineering activities

Juliette Mattioli
Souhaiel Khalfaoui
  • Fonction : Auteur
  • PersonId : 768857
  • IdRef : 169958590
Freddy Lecue
  • Fonction : Auteur
  • PersonId : 1114750
Henri Sohier
  • Fonction : Auteur
  • PersonId : 1054948
Frédéric Jurie

Résumé

This paper reviews the entire engineering process of trustworthy Machine Learning (ML) algorithms designed to equip critical systems with advanced analytics and decision functions. We start from the fundamental principles of ML and describe the core elements conditioning its trust, particularly through its design: namely domain specification, data engineering, design of the ML algorithms, their implementation, evaluation and deployment. The latter components are organized in an unique framework for the design of trusted ML systems.
Fichier principal
Vignette du fichier
2209.15438.pdf (591.73 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03808323 , version 1 (10-10-2022)

Identifiants

Citer

Juliette Mattioli, Agnes Delaborde, Souhaiel Khalfaoui, Freddy Lecue, Henri Sohier, et al.. Empowering the trustworthiness of ML-based critical systems through engineering activities. 2022. ⟨hal-03808323⟩
131 Consultations
42 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More