Collateral Damage Assessment Model for AI System Target Engagement in Military Operations
Clara Maathuis, Kasper Cools
TL;DR
The paper addresses collateral damage in AI-enabled target engagements within military operations, with a focus on non-kinetic targeting of AI systems. It proposes a layered Knowledge Representation and Reasoning framework, CDAIMO, built via Design Science Research to capture temporal, spatial, and force dimensions and to integrate AI components, datasets, and civilian linkages. By combining qualitative and quantitative metrics (severity and likelihood) with formal rules, it enables auditable, proportionality-driven decision making. The authors illustrate the approach through a cyber operation use-case, showing how data quality and rule-based reasoning yield mitigation recommendations while adhering to legal and ethical constraints. Overall, the work advances responsible, trustworthy military AI by providing a modular, auditable framework that bridges kinetic and non-kinetic elements and sets the stage for further exploration with LLMs and reinforcement learning.
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.