Mechanical Conscience: A Mathematical Framework for Dependability of Machine Intelligenc

The paper presents a novel mathematical framework called mechanical conscience (MC) aimed at ensuring the dependability of machine intelligence in distributed collaborative intelligence (DCI) environments. This framework introduces a supervisory filter that adjusts agent actions to minimize deviations from acceptable behavioral norms at the trajectory level, addressing limitations of existing methods that focus on individual actions. The theoretical constructs of conscience score, mechanical guilt, and resonant dependability provide new governance signals, making it crucial for practitioners to manage emergent risks in multi-agent systems effectively.