Accountability Judgments and Ethical Decision Modules across Public Safety Robots

Abstract

The integration of autonomous systems into public safety domains necessitates a profound understanding of how algorithmic decision-making architectures influence human perceptions of accountability. This paper investigates the intricate relationship between embedded ethical decision modules in public safety robots and the subsequent accountability judgments made by human stakeholders. By utilizing agent-based modeling combined with human-in-the-loop perceptual evaluations, this study simulates complex emergency scenarios where autonomous agents must resolve moral dilemmas. We contrast three distinct ethical architectures, namely utilitarian, deontological, and hybrid rule-based modules, to determine how the algorithmic rationale impacts the attribution of blame and responsibility. The findings indicate a significant divergence in accountability judgments based on the underlying ethical framework of the robot. Specifically, utilitarian modules, which optimize for the greatest overall survival rate, tend to diffuse perceived accountability among developers and operators, whereas deontological modules, which adhere to strict operational constraints, concentrate blame on the autonomous agent itself. This research provides empirical evidence bridging machine ethics and social psychology, offering a comprehensive framework for policymakers, legal scholars, and roboticists. The insights derived from our agent modeling simulations underscore the critical need for transparent ethical architectures in autonomous systems to ensure societal trust and legal clarity in public safety deployments.