通过工程活动增强基于ML的关键系统的可信度
Empowering the trustworthiness of ML-based critical systems through engineering activities
摘要：
本文回顾了值得信赖的机器学习（ML）算法的整个工程过程，这些算法旨在为关键系统提供高级分析和决策功能。我们从ML的基本原则开始，并描述了制约其信任的核心要素，特别是通过其设计：即域规范，数据工程，ML算法的设计，它们的实现，评估和部署。后者的组件被组织在一个独特的框架中，用于设计可信的ML系统。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
