manipulation-detection

Manipulation Detection in Retail AI: Causal Inference for the Personalization–Manipulation Boundary

Defining the mathematical boundary between helpful personalization and harmful manipulation using causal reasoning and responsibility gates

Retail recommendation systems operate between beneficial personalization and potentially manipulative behavior. This paper introduces a causal-inference framework that defines the personalization-manipulation boundary, enabling retail AI agents to operate within explicit ethical constraints while routing boundary violations to human review.