arXiv:2605.30632v1 Announce Type: cross Abstract: 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, facilitatin
The proliferation of advanced AI models like LLMs necessitates better methods for human-AI collaboration to optimize their utility and ensure safety.
This framework addresses a core challenge in human-AI interaction, aiming to improve reliability, interpretability, and trust in AI-assisted decision-making.
The explicit conceptualization of human-AI collaboration through defined role-pairs could lead to more structured and effective integration of AI into complex analytical tasks.
- · AI developers
- · Analytics professionals
- · Consulting firms
- · LLM providers
- · Tasks requiring opaque AI decision-making
- · Systems lacking interpretability
- · Unstructured human-AI interfaces
Improved human-AI collaboration tools based on shared semantic reasoning will emerge.
This will accelerate the adoption of AI in critical reasoning roles across various industries.
The development of a common 'reasoning language' could eventually lead to more robust and generalized AI agents capable of higher-level cognitive functions.
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Read at arXiv cs.LG