This position paper argues that robust AI personalization requires a Human Context Protocol (HCP): a user-owned, secure, and interoperable preference layer that grants individuals granular, revocable control over how their data steers AI systems. By replacing siloed, behavior-inferred signals with direct preference articulation, HCP unifies fragmented data, lowers switching costs, and enables seamless portability across AI services, fostering a more competitive ecosystem. We outline core design principles \u2013 natural-language preference storage, scoped sharing, and strong authentication with revocation \u2013 that extend earlier personal-data architectures to the scale and stakes of modern generative AI. Centering control in users, HCP is not merely a technical convenience but a necessary foundation for AI systems that are genuinely personal, interoperable, and aligned with diverse human values.<\/p>\n","protected":false},"excerpt":{"rendered":"
This position paper argues that robust AI personalization requires a Human Context Protocol (HCP): a user-owned, secure, and interoperable preference layer that grants individuals granular, revocable control over how their data steers AI systems. By replacing siloed, behavior-inferred signals with direct preference articulation, HCP unifies fragmented data, lowers switching costs, and enables seamless portability across […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[{"type":"text","value":"Anand Shah","user_id":0},{"type":"text","value":"Tobin South","user_id":0},{"type":"text","value":"Talfan Evans","user_id":0},{"type":"text","value":"Hannah Rose Kirk","user_id":0},{"type":"text","value":"Andrew Trask","user_id":0},{"type":"user_nicename","value":"E. 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