AI-Based Safety-Critical Systems (AI-SCS) are being increasingly deployed in the real world. These can pose a risk of harm to people and the environment. Reducing that risk is an overarching priority during development and operation. As more AI-SCS become autonomous, a layer of risk management via human intervention has been removed. Following an accident it will be important to identify causal contributions and the different responsible actors behind those to learn from mistakes and prevent similar future events. Many authors have commented on the "responsibility gap" where it is difficult for developers and manufacturers to be held responsible for harmful behaviour of an AI-SCS. This is due to the complex development cycle for AI, uncertainty in AI performance, and dynamic operating environment. A human operator can become a "liability sink" absorbing blame for the consequences of AI-SCS outputs they weren't responsible for creating, and may not have understanding of. This cross-disciplinary paper considers different senses of responsibility (role, moral, legal and causal), and how they apply in the context of AI-SCS safety. We use a core concept (Actor(A) is responsible for Occurrence(O)) to create role responsibility models, producing a practical method to capture responsibility relationships and provide clarity on the previously identified responsibility issues. Our paper demonstrates the approach with two examples: a retrospective analysis of the Tempe Arizona fatal collision involving an autonomous vehicle, and a safety focused predictive role-responsibility analysis for an AI-based diabetes co-morbidity predictor. In both examples our primary focus is on safety, aiming to reduce unfair or disproportionate blame being placed on operators or developers. We present a discussion and avenues for future research.

AI-基于安全关键系统（AI-SCS）在现实世界中的部署越来越多。降低风险是开发和运营中的首要任务。本文考虑了责任的不同方面（角色、道德、法律和因果关系），以及它们在AI-SCS安全背景下的应用。我们使用一个核心概念（演员（A）对事件（O）负责）来创建角色责任模型，从而产生了一种捕捉责任关系并澄清以前已知的责任问题的实用方法。通过对事故案例和基于AI的糖尿病并发症预测器的安全性分析，我们的主要目标是在安全性方面减少对操作员或开发人员的不公平或不成比例的责备。我们提出了进一步研究的讨论和方向。

AI基准安全系统的责任建模