论文标题
机器学习安全保证的因果模型
A causal model of safety assurance for machine learning
论文作者
论文摘要
本文提出了一个基于因果关系模型的框架，可以在该模型上建立有效的安全保证案例。在此过程中，我们基于确定的安全工程原则以及以前关于ML结构保证论点的工作。本文定义了四类安全案例证据和结构化分析方法，可以有效地结合这些证据。在适当的情况下，使用这些贡献的抽象形式来说明他们评估的因果关系，它们对证据的安全论点和理想特性的贡献。根据提议的框架，重新评估了该领域的进展，并提出了一系列未来的研究方向，以实现该领域的切实进步。
This paper proposes a framework based on a causal model of safety upon which effective safety assurance cases for ML-based applications can be built. In doing so, we build upon established principles of safety engineering as well as previous work on structuring assurance arguments for ML. The paper defines four categories of safety case evidence and a structured analysis approach within which these evidences can be effectively combined. Where appropriate, abstract formalisations of these contributions are used to illustrate the causalities they evaluate, their contributions to the safety argument and desirable properties of the evidences. Based on the proposed framework, progress in this area is re-evaluated and a set of future research directions proposed in order for tangible progress in this field to be made.
