Navigating Epistemic Monocultures in AI-Driven Science: A Simulation Study

2026-08-19

AI Summary

研究探讨AI在科学中的应用可能导致的认知单一化问题，通过NK模型模拟，提出随机化与个性化两种解决方法以增强多样性。

Abstract

AI integration into scientific communities promises accelerated discovery but raises concerns about detrimental homogenization. We develop an NK landscape model to explore these promises and risks. We find that non-personalized AI systems that offer uniform guidance yield benefits only under a narrow conjunction of problem structure, practices, and baseline research capabilities, becoming harmful otherwise. We implement two proposed mitigations: randomization and personalization. While randomization's utility remains restricted to decomposable problems, personalization can enhance diversity, enabling benefits across a broader range of conditions. Crucially, these benefits are not automatic, but depend on effective institutional adaptation, requiring new standards and practices.

Problem

• AI integration
• scientific communities
• epistemic monocultures
• non-personalized AI
• uniform guidance

Innovation

• NK landscape model
• personalization
• randomization
• institutional adaptation