We introduce Rationalize, a role-pair framework for shared semantic reasoning between humans and AI models in data-driven sensemaking. Building on ideas in human-machine teaming and critical thinking, we conceptualize human-AI interaction as a series of complementary role pairs (Explorer-Guide, Investigator-Informant, Teacher-Student, Judge-Advocate) operating in a shared reasoning space. In this space, human analysts and AI models (such as LLMs) make purposes, questions, assumptions, evidence, inferences, and implications explicit, facilitating alignment not only at the output level but at the level of rationalization of intent and action by each side. We relate these role pairs to the bidirectional human-AI alignment framework, illustrating how "aligning AI to humans" and "aligning humans to AI" differ by role, and sketch a collaborative research agenda for alignment design and assessment using element-level and role-specific approaches.

翻译：本文提出Rationalize框架，一种用于数据驱动意义构建中人类与AI模型间共享语义推理的角色-配对框架。基于人机协作与批判性思维理念，我们将人机交互概念化为一系列互补角色对（探索者-引导者、调查者-通报者、教师-学生、评判者-倡导者），它们在一个共享推理空间中运作。在此空间中，人类分析师与AI模型（如大语言模型LLM）能够使目标、问题、假设、证据、推理与隐含意义显式化，不仅促进输出层面的对齐，更推动双方在意图与行动合理化层面的对齐。我们将这些角色对与双向人机对齐框架相联系，阐释"AI对人类对齐"与"人类对AI对齐"如何因角色不同而产生差异，并勾勒出使用元素级与角色特定方法进行对齐设计与评估的协作研究议程。