我的角色是什么？为基于AI的安全关键系统建模责任
What's my role? Modelling responsibility for AI-based safety-critical systems
AI总结：
本文针对AI安全关键系统中的责任鸿沟，提出基于“行动者对事件负责”的角色责任建模方法，并通过自动驾驶事故和糖尿病共病预测两个案例验证其厘清责任、减少不当归责的价值。
摘要：
基于AI的安全关键系统（AI-SCS）正越来越多地部署于现实世界。这些系统可能对人员和环境造成伤害风险。在开发和运行期间，降低该风险是一项首要优先事项。随着更多AI-SCS变得自主，通过人工干预进行风险管理的一个层级已被移除。事故发生后，重要的是识别因果贡献及其背后不同的责任行动者，以便从错误中学习并防止未来发生类似事件。许多作者评论过“责任鸿沟”：开发者和制造商很难为AI-SCS的有害行为承担责任。这源于AI复杂的开发周期、AI性能的不确定性以及动态运行环境。人类操作者可能成为“责任沉淀池”，为AI-SCS输出所造成的后果承受指责，而这些输出并非由他们负责创建，他们也可能并不理解。这篇跨学科论文考察了责任的不同含义（角色责任、道德责任、法律责任和因果责任），以及它们如何适用于AI-SCS安全语境。我们使用一个核心概念（行动者(A)对事件(O)负责）来创建角色责任模型，从而产生一种实用方法，用于捕获责任关系，并澄清此前识别出的责任问题。本文通过两个示例展示该方法：对亚利桑那州坦佩市一起涉及自动驾驶车辆的致命碰撞进行回顾性分析，以及对一个基于AI的糖尿病共病预测器进行以安全为中心的预测性角色责任分析。在两个示例中，我们的主要重点都是安全，旨在减少不公平或不成比例地归咎于操作者或开发者的指责。我们还提出了讨论和未来研究方向。
英文摘要：
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.
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。
