让人类与AI在分析数据时共同理清思路，提升协作理解力。
Rationalize: Shared Semantic Reasoning for Human-AI Alignment
- 设计角色对框架，实现人与AI在推理过程中的互补协作。
- 通过显式表达假设、证据和推论，促进双方意图对齐。
- 适合研究人机协作、认知对齐或提示工程的学者参考。
我们提出Rationalize，一种用于人与人工智能模型在数据驱动的分析任务中实现共享语义推理的角色对框架。基于人机协同与批判性思维理念，将人机互动建模为一系列互补角色对（探索者-引导者、调查者-信息提供者、教师-学生、裁判-辩护者），在共享推理空间中，人类分析师与AI模型（如大语言模型）共同明确其目的、问题、假设、证据、推论及含义，不仅实现输出对齐，更在意图与行为的合理性层面达成一致。该框架与双向人机对齐理论关联，揭示了不同角色下‘对齐AI至人类’与‘对齐人类至AI’的差异，并提出了基于元素级与角色特异性方法的协作对齐设计与评估研究议程。
原文摘要 · Abstract (English)
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.
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