AI Social Responsibility as Reachability: Execution-Level Semantics for the Social Responsibility Stack
Otman Basir

TL;DR
This paper formalizes social responsibility in AI systems as a reachability problem within execution trajectories, using Petri nets to ensure responsible behavior in complex, feedback-driven environments.
Contribution
It introduces a formal foundation for the Social Responsibility Stack based on reachability semantics and employs Petri nets to operationalize responsibility in autonomous systems.
Findings
Responsibility is modeled as a reachability property of system execution.
Petri nets encode responsibility constraints and enable monitoring of inadmissible states.
Embedding reachability in SRS internalizes responsibility as a structural invariant.
Abstract
Artificial intelligence systems are increasingly embedded as persistent, closed-loop components within cyber-physical, social, and institutional processes. Rather than producing isolated outputs, such systems operate continuously under feedback, adaptation, and scale, reshaping physical flows, human behavior, and institutional practice over time. In these settings, socially unacceptable outcomes rarely arise from singular faults or explicit policy violations. Instead, they emerge through cumulative execution trajectories enabled by repetition, concurrency, and feedback. This paper advances the formal foundation of the Social Responsibility Stack (SRS) by making its central requirement explicit: responsibility is fundamentally a reachability property of system execution. A system is responsible iff its execution semantics prevent entry into inadmissible global configurations,…
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Taxonomy
TopicsSmart Grid Security and Resilience · Safety Systems Engineering in Autonomy · Petri Nets in System Modeling
