Computational Architectures for Responsible Autonomous Systems

Abstract

Responsible autonomous systems (RAS) -- including autonomous vehicles, robotic assistants, and AI-driven decision agents -- must satisfy stringent requirements for safety, explainability, human oversight, and value alignment in dynamic, uncertain real-world environments. Existing autonomous system architectures prioritise performance and efficiency but lack principled mechanisms for embedding responsibility properties at the computational architecture level. This paper proposes and evaluates the Responsibility-Aware Architecture (RAA) framework, which augments classical deliberative-reactive autonomous system architectures with four responsibility modules: a Safety Envelope Monitor (SEM), an Explainable Decision Logger (EDL), a Human Override Controller (HOC), and a Value Alignment Verifier (VAV). The RAA framework is evaluated through simulation experiments on three autonomous system testbeds -- urban navigation, warehouse robotics, and clinical decision agent -- and through expert assessment by 28 autonomous systems engineers. Simulation results demonstrate that RAA-equipped systems achieve a 38.6% reduction in safety-critical incidents (SD = 4.9%), a 44.2% improvement in decision explainability scores (SD = 5.8%), and a 27.3% improvement in value alignment consistency (SD = 3.7%) relative to standard architectures. Expert assessment confirms high perceived utility of all four RAA modules (mean rating = 4.3/5.0, SD = 0.6). The study contributes a reference RAA specification, a responsibility testbed benchmark suite, and empirical evidence for computational architecture as a primary mechanism for responsible autonomous system design.