Choice Vectors: Streamlining Personal AI Alignment Through Binary Selection

Abstract

Value alignment for AI is not “one-size-fits-all”: even polite and friendly models can still fail to represent individual user contexts and preferences, and local cultural norms. This paper presents a modular workflow for personal fine-tuning, synthesizing four core components from our previous research: (1) robust vectorization of user values and preferences, (2) a binary choice user interface (UI) approach to capturing those preferences with minimal cognitive load, (3) contrastive activation methods for steering large language models (LLMs) via difference vectors, and (4) knowledge graph integration for more auditable and structured alignment. Our approach—descended from past research on “Towards an End-to-End Personal Fine-Tuning Framework”—demonstrates how these elements can be combined to create personalized, context-rich alignment solutions. We report on user studies for the forced-choice UI, describe an experimental pipeline for deriving “control vectors”, and propose a “moral graph” method for bridging symbolic and vector-based alignment. Our findings suggest that multi-pronged personalization can significantly reduce user annotation fatigue, improve alignment fidelity, and allow for more flexible, interpretable AI behaviors.