Position: Robust AI Personalization Will Require a Human Context Protocol
Anand Shah, Tobin South, Talfan Evans, Hannah Kirk, Jiaxin Pei, Andrew Trask, Glen Weyl, Michiel Bakker
Abstract
Personalization underpins the modern digital economy. Today, personalization is largely implemented through provider-managed infrastructure that infers user preferences from behavioral data, with limited portability or user control. However, large language models (LLMs) are increasingly being used to perform tasks on users' behalf. The age of LLMs for the first time provides a path to a more controllable and interpretable personalization paradigm, grounded in user-expressed natural language preferences and context. In this position paper, we argue that to provide robust and user-centric personalization, we need a new Human Context Protocol (HCP) to represent and share personal preferences across AI systems. HCP treats preferences as a portable, user-governed layer in the personalization stack, enabling interoperability, scoped access, and revocation. Along with a working prototype to ground discussion, we consider counterarguments along adoption dynamics and market incentives, high-stakes use cases, and outline novel paths via the HCP towards trustworthy personalization in the human-AI economy.
BibTeX
@inproceedings{icml2026_positionrobustai,
title = {Position: Robust AI Personalization Will Require a Human Context Protocol},
author = {Anand Shah and Tobin South and Talfan Evans and Hannah Kirk and Jiaxin Pei and Andrew Trask and Glen Weyl and Michiel Bakker},
booktitle = {ICML 2026},
year = {2026}
}