The pervasive integration of Artificial Intelligence (AI) has introduced complex challenges in the responsibility and accountability in the event of incidents involving AI-enabled systems. The interconnectivity of these systems, ethical concerns of AI-induced incidents, coupled with uncertainties in AI technology and the absence of corresponding regulations, have made traditional responsibility attribution challenging. To this end, this work proposes a Computational Reflective Equilibrium (CRE) approach to establish a coherent and ethically acceptable responsibility attribution framework for all stakeholders. The computational approach provides a structured analysis that overcomes the limitations of conceptual approaches in dealing with dynamic and multifaceted scenarios, showcasing the framework's explainability, coherence, and adaptivity properties in the responsibility attribution process. We examine the pivotal role of the initial activation level associated with claims in equilibrium computation. Using an AI-assisted medical decision-support system as a case study, we illustrate how different initializations lead to diverse responsibility distributions. The framework offers valuable insights into accountability in AI-induced incidents, facilitating the development of a sustainable and resilient system through continuous monitoring, revision, and reflection.

翻译：人工智能的广泛集成给涉及AI系统的突发事件的责任与问责带来了复杂挑战。这些系统的互联性、AI引发事件的伦理问题，加上AI技术的不确定性及相应监管的缺失，使得传统责任归属变得困难。为此，本文提出一种计算性反思均衡方法，为所有利益相关者建立一致且伦理上可接受的责任归属框架。该计算方法提供了结构化分析，克服了概念性方法在处理动态及多层面场景时的局限性，展示了该框架在责任归属过程中的可解释性、连贯性和适应性。我们研究了主张的初始激活水平在均衡计算中的关键作用。以AI辅助医疗决策支持系统为案例，我们阐释了不同初始化方式如何导致不同的责任分配。该框架为AI引发事件中的问责提供了宝贵见解，通过持续监控、修正与反思，促进可持续且有韧性的系统开发。