Rationalize: Shared Semantic Reasoning for Human-AI Alignment

Researchers introduce Rationalize, a framework enabling shared semantic reasoning between humans and AI models through complementary role pairs (Explorer-Guide, Investigator-Informant, Teacher-Student, Judge-Advocate). The framework aims to align AI systems not just at the output level but by making purposes, questions, assumptions, and evidence explicit during human-AI collaboration, addressing bidirectional alignment challenges.

Rationalize represents a methodological advancement in human-AI alignment by moving beyond output-level agreement to collaborative reasoning transparency. Rather than treating alignment as a unidirectional process of constraining AI behavior, the framework reconceptualizes human-AI interaction as complementary role-switching where both parties articulate underlying logic. This addresses a fundamental challenge in AI deployment: humans and systems often reach identical conclusions through incompatible reasoning paths, creating brittle trust and poor generalization.

The framework builds on established human-factors research in teaming and critical thinking but applies it specifically to large language models and data-driven analysis. By structuring interaction around explicit purposes, questions, assumptions, evidence, inferences, and implications, Rationalize creates accountability at the reasoning level rather than merely monitoring outputs. The role-pair concept acknowledges that alignment requirements differ contextually—what it means to "align AI to humans" when humans are judges differs fundamentally from when they're students.

For AI developers and organizations deploying LLMs in high-stakes domains like financial analysis, policy evaluation, or scientific research, this framework offers practical scaffolding for validation and auditing. Rather than black-box systems requiring post-hoc interpretability, Rationalize-informed systems produce auditable reasoning traces. This has direct implications for regulatory compliance and institutional risk management. The bidirectional framing also addresses a gap in current alignment literature, which predominantly focuses on controlling AI rather than supporting human adaptation to AI capabilities.

• →Framework enables transparency at the reasoning level, not just outputs, improving human trust and system accountability
• →Role-pair structure (Explorer-Guide, etc.) makes alignment requirements context-dependent rather than uniform
• →Applicability to high-stakes domains like finance, policy, and research where auditable reasoning is critical
• →Addresses bidirectional alignment by acknowledging humans must adapt to AI while AI must align to human intent
• →Provides practical scaffolding for LLM deployment in institutions requiring regulatory compliance and transparency