Robust AI Personalization Will Require a Human Context Protocol

This paper proposes the Human Context Protocol (HCP), a technical framework designed to give individuals direct control over how their personal preferences shape AI interactions. Currently, AI personalization relies on fragmented data silos and behavioral inferences that often fail to reflect a user’s true intent or values. By establishing a user-owned preference layer, the protocol allows people to securely store and share specific subsets of their data across different AI services using natural language. This architecture aims to reduce provider lock-in and ensure that artificial intelligence remains aligned with diverse human perspectives. Ultimately, the authors argue that such a system is a legal and ethical necessity for fostering a competitive, transparent, and truly personalized digital ecosystem.