论文标题
通过工程活动增强基于ML的关键系统的信任度
Empowering the trustworthiness of ML-based critical systems through engineering activities
论文作者
论文摘要
本文回顾了可信赖的机器学习（ML）算法的整个工程过程，旨在为关键系统配备具有先进的分析和决策功能。我们从ML的基本原理开始，并描述其信任的核心要素，尤其是通过其设计：即域规范，数据工程，ML算法的设计，其实现，评估和部署。后一个组件是在一个独特的框架内组织的，用于设计受信任的ML系统。
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.
