  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.
