在AI驱动的科学中应对认知单一文化：一项模拟研究
Navigating Epistemic Monocultures in AI-Driven Science: A Simulation Study
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中文总结 AI 辅助
本研究通过NK景观模型模拟，探究AI驱动科学中认知单一文化的风险，发现非个性化AI仅在特定条件下有益，随机化仅适用于可分解问题，个性化可提升多样性，且需制度适应才能发挥效用。
中文摘要 AI 辅助
将AI整合进科学共同体有望加速发现，但也引发了关于有害同质化的担忧。我们开发了NK景观模型以探究这些机遇与风险。研究发现，提供统一指导的非个性化AI系统仅在问题结构、实践及基线研究能力的特定组合下才会产生益处，否则会造成危害。我们实施了两种拟议的缓解措施：随机化与个性化。随机化的效用仅局限于可分解问题，而个性化可提升多样性，使其在更广泛的条件下发挥益处。重要的是，这些益处并非自动实现，而是依赖于有效的制度适应，需要新的标准与实践。
英文摘要
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.
