Collateral Damage Assessment Model for AI System Target Engagement in Military Operations
This addresses the need for rigorous collateral damage assessment in military AI targeting, though it appears incremental as it builds on existing KRR methods.
The paper tackles the problem of assessing collateral damage when AI systems are targeted in military operations by introducing a novel model that integrates temporal, spatial, and force dimensions using a Knowledge Representation and Reasoning architecture, with evaluation through instantiation to support responsible and trustworthy intelligent systems.
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.