ALGORITHMIC ATTRIBUTION: THE PROMISE AND PERIL OF AI-DRIVEN CHEMICAL FORENSICS FOR THE CHEMICAL WEAPONS CONVENTION REGIME

Abstract

The integration of artificial intelligence and machine learning into chemical forensics represents a transformative yet precarious shift for the verification architecture of the Chemical Weapons Convention. While computational approaches such as deep neural network spectral interpretation, non target environmental analysis, and automated impurity profiling offer unprecedented analytical speed and sensitivity, they collide directly with a treaty regime designed around human expert judgment, physical custody chains, and finite chemical schedules. This paper interrogates the legal, political, and structural consequences of allowing algorithmic models to mediate international chemical weapons attribution. The paper argues that the technical opacity of advanced machine learning models compromises established norms of scientific reproducibility and procedural fairness, while the proliferation of generative molecular design tools threatens to render traditional schedule based control lists obsolete. Furthermore, because advanced computational infrastructure remains concentrated among a handful of technologically privileged states, the uncritical deployment of these tools risks entrenching an algorithmic verification divide that could severely erode institutional trust in the Organisation for the Prohibition of Chemical Weapons. To resolve these challenges, the paper presents a comprehensive governance architecture founded on methodological explainability, multilateral cross laboratory validation, equitable capacity transfer, and the strict preservation of human interpretive authority in high stakes compliance decisions.