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

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 thro...

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Veröffentlicht in:	arXiv.org 2022-09
Hauptverfasser:	Mattioli, Juliette, Delaborde, Agnes, Khalfaoui, Souhaiel, Lecue, Freddy, Sohier, Henri, Jurie, Frederic
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Sprache:	eng
Schlagworte:	Algorithms Decision analysis Machine learning
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creator	Mattioli, Juliette Delaborde, Agnes Khalfaoui, Souhaiel Lecue, Freddy Sohier, Henri Jurie, Frederic
description	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.
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subjects	Algorithms Decision analysis Machine learning
title	Empowering the trustworthiness of ML-based critical systems through engineering activities
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