PREreview del Governing Thought: An Analytic Grammar for Diagnosing Epistemic Risk in Generative AI–Scholar Engagement

Summary

This paper introduces the Governing Thought Framework (GTF) to help researchers evaluate how they use generative AI. Rather than treating AI use as a simple "yes or no" choice, the author breaks human-AI collaboration down into four distinct modes: Assistant, Surrogate Thinker, Dialogic Interlocutor, and Epistemic Proxy. This creates a clear, structured way to analyze who actually holds authority, directs the workflow, and takes ultimate responsibility for the work.

Key Strengths

1. A Practical Model for AI Roles: Treating AI as a spectrum—from a simple Assistant (handling basic tasks) to a Surrogate Thinker (shaping ideas)—accurately reflects how researchers actually interact with these tools.

2. Highlights Compounding Risk: The paper correctly identifies the long-term risk of unverified AI outputs: if an early synthetic idea goes unchecked, it builds up errors throughout the rest of the research process.

3. Clear Boundary on Responsibility: Defining Epistemic Abdication creates a useful line between ordinary task delegation and completely giving up human oversight.

Main Suggestions & Areas for Improvement

• Making the Boundaries Audit-Friendly: While separating "shaping the ideas" from "controlling the process" makes sense in theory, measuring this in real life is tough. The framework needs practical, observable indicators so risk managers or ethics boards can actually tell when a user has quietly handed over control to the AI.

• Accounting for Human Fatigue: People get tired and rely on automation over time. An interaction that starts as an active back-and-forth (Dialogic Interlocutor) can easily slide into passive approval (Epistemic Proxy) over a long work session. Adding a dynamic that accounts for operator fatigue would make the model stronger.

• Connecting to Institutional Risk: To turn this from an academic model into an actionable tool for organizations, the paper should explain how these categories connect directly to existing compliance, auditing, or model risk management systems.

Conclusion

An insightful, highly practical paper that moves the AI governance conversation past simple adoption metrics. It gives researchers, ethics boards, and risk officers a clear vocabulary for managing human-in-the-loop decisions.

Competing interests

The author declares that they have no competing interests.

Use of Artificial Intelligence (AI)

The author declares that they used generative AI to come up with new ideas for their review.