Empowering the trustworthiness of ML-based critical systems through engineering activities
Software Engineering
2022-10-03 v1 Machine Learning
Abstract
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.
Keywords
Cite
@article{arxiv.2209.15438,
title = {Empowering the trustworthiness of ML-based critical systems through engineering activities},
author = {Juliette Mattioli and Agnes Delaborde and Souhaiel Khalfaoui and Freddy Lecue and Henri Sohier and Frederic Jurie},
journal= {arXiv preprint arXiv:2209.15438},
year = {2022}
}
Comments
This work has been supported by the French government under the "France 2030" program, as part of the SystemX Technological Research Institute Research Institute