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This study employs an NK landscape model to investigate the impact of AI integration on scientific communities, highlighting the risks of epistemic monocultures that arise from uniform AI guidance. The findings reveal that while non-personalized AI can be beneficial under specific conditions, it can also lead to detrimental outcomes in diverse contexts. Implementing randomization and personalization strategies shows promise in enhancing diversity and mitigating risks, but their effectiveness hinges on institutional adaptation and the establishment of new standards and practices.
Non-personalized AI systems can inadvertently harm scientific diversity, but personalization strategies unlock broader benefits—if institutions adapt effectively.
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: they depend on effective institutional adaptation, requiring new standards, protocols, and practices.