Runtime Enforcement for Operationalizing Ethics in Autonomous Systems

TL;DR

This paper presents SLEEC@run.time, a runtime enforcement framework using ASM formalism and MAPE-K architecture to operationalize and adapt ethical rules in autonomous systems, demonstrated on an assistive robot.

Contribution

It introduces a novel runtime enforcement approach for ethical rules in autonomous systems using formal ASM models and a control-loop architecture, enabling dynamic ethical behavior adaptation.

Findings

Effective ethical adherence with negligible overhead

Supports dynamic addition/removal of ethical rules

Demonstrated on assistive robot scenario

Abstract

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.