Collateral Damage Assessment Model for AI System Target Engagement in Military Operations

TL;DR

This paper introduces a comprehensive model for assessing collateral damage caused by AI systems in military target engagement, integrating multiple factors within a knowledge-based reasoning framework to promote responsible use.

Contribution

It presents a novel layered assessment model that combines temporal, spatial, and force considerations within a unified architecture for military AI targeting.

Findings

Model effectively captures damage categories and system components.

Provides transparent reasoning for damage assessment.

Serves as a foundation for developing trustworthy military AI systems.

Abstract

In an era where AI (Artificial Intelligence) systems play an increasing role in the battlefield, ensuring responsible targeting demands rigorous assessment of potential collateral effects. In this context, a novel collateral damage assessment model for target engagement of AI systems in military operations is introduced. The model integrates temporal, spatial, and force dimensions within a unified Knowledge Representation and Reasoning (KRR) architecture following a design science methodological approach. Its layered structure captures the categories and architectural components of the AI systems to be engaged together with corresponding engaging vectors and contextual aspects. At the same time, spreading, severity, likelihood, and evaluation metrics are considered in order to provide a clear representation enhanced by transparent reasoning mechanisms. Further, the model is demonstrated and evaluated through instantiation which serves as a basis for further dedicated efforts that aim at building responsible and trustworthy intelligent systems for assessing the effects produced by engaging AI systems in military operations.