Ethics Policy
Research Suggests a New Way to Combine Human Intuition and AI into a Transparent System
A new AI study proposes a method to dismantle two different 'black boxes': human expert intuition and the difficult-to-interpret decision-making of AI. The goal is to transform these into a transparent, examinable, and expandable system that supports joint thinking between humans and AI.
The work introduces the concept of 'human and AI collaborative cognitive enhancement.' Here, human expertise—such as a doctor's diagnostic thinking or a teacher's educational instinct—is transferred to a machine as clearly defined, reusable building blocks of thought. According to the research, these can be packaged into what is called an adjunctive thinking framework, a computational data package that can be both extracted from expert discussions and integrated into an AI system.
In practice, the package is uploaded to the Recursive Adversarial Meta-Thinking Network introduced by the researcher, a network whose name refers to recurring, mutually challenging cycles of thought. The network does not merely provide answers but aims to mimic and reuse the reasoning logic of experts in a way that can be examined and audited.
The research emphasizes so-called meta-interaction: a constructed, layered dialogue between humans and AI, where not only answers are exchanged but also the rules of thinking. This is claimed to open up the possibility of scaling expert thinking, previously tied to individual humans, to new tasks—and at the same time offering a new basis for AI regulation and governance, as the principles of decision-making can be made visible.
Source: Deconstructing the Dual Black Box: A Plug-and-Play Cognitive Framework for Human-AI Collaborative Enhancement and Its Implications for AI Governance, ArXiv (AI).
The work introduces the concept of 'human and AI collaborative cognitive enhancement.' Here, human expertise—such as a doctor's diagnostic thinking or a teacher's educational instinct—is transferred to a machine as clearly defined, reusable building blocks of thought. According to the research, these can be packaged into what is called an adjunctive thinking framework, a computational data package that can be both extracted from expert discussions and integrated into an AI system.
In practice, the package is uploaded to the Recursive Adversarial Meta-Thinking Network introduced by the researcher, a network whose name refers to recurring, mutually challenging cycles of thought. The network does not merely provide answers but aims to mimic and reuse the reasoning logic of experts in a way that can be examined and audited.
The research emphasizes so-called meta-interaction: a constructed, layered dialogue between humans and AI, where not only answers are exchanged but also the rules of thinking. This is claimed to open up the possibility of scaling expert thinking, previously tied to individual humans, to new tasks—and at the same time offering a new basis for AI regulation and governance, as the principles of decision-making can be made visible.
Source: Deconstructing the Dual Black Box: A Plug-and-Play Cognitive Framework for Human-AI Collaborative Enhancement and Its Implications for AI Governance, ArXiv (AI).
This text was generated with AI assistance and may contain errors. Please verify details from the original source.
Original research: Deconstructing the Dual Black Box:A Plug-and-Play Cognitive Framework for Human-AI Collaborative Enhancement and Its Implications for AI Governance
Publisher: ArXiv (AI)
Authors: Yiming Lu
December 27, 2025
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