Runtime Enforcement for Operationalizing Ethics in Autonomous Systems
For developers of autonomous systems, this work provides a practical method to dynamically enforce ethical rules, but it is an incremental application of existing control-loop and formal methods to the ethics domain.
This paper introduces SLEEC@run.time, a runtime enforcement approach for operationalizing ethical rules in autonomous systems using the MAPE-K architecture and ASM formalism. Evaluation on an assistive robot scenario shows it ensures ethical adherence with negligible execution time overhead.
This paper addresses the challenge of operationalizing ethics in autonomous systems through runtime enforcement. It first conceptualizes the system's ethical space and outlines a structured ethics assurance process. Building on this foundation, it introduces an enforcement subsystem that operationalizes ethical rules, specifically social, legal, ethical, empathetic, and cultural (SLEEC) requirements, through the Abstract State Machine (ASM) formalism. The enforcement subsystem is built on the MAPE-K control-loop architecture for monitoring and controlling the system's ethical behavior, and it relies on an ASM-based runtime model of the ethical rules to enforce. This enables the dynamic evaluation, adaptation, and enforcement of ethical behavior within a runtime formal model. The overall approach, named SLEEC@run.time, is demonstrated on an assistive robot scenario, showcasing how both the robot's behavior and the governing ethical rules can dynamically adapt to contextual changes. By leveraging a flexible runtime model, SLEEC@run.time accommodates changes such as the addition or removal of SLEEC rules, ensuring a robust and evolvable approach to ethical assurance in autonomous systems. The evaluation of SLEEC@run.time shows that it effectively ensures the system's adherence to ethical principles with negligible execution time overhead.