AI for Science (AI4Science), particularly in the form of self-driving labs, has the potential to sideline human involvement and hinder scientific discovery within the broader community. While prior research has focused on ensuring the responsible deployment of AI applications, enhancing security, and ensuring interpretability, we also propose that promoting openness in AI4Science discoveries should be carefully considered. In this paper, we introduce the concept of AI for Open Science (AI4OS) as a multi-agent extension of AI4Science with the core principle of maximizing open knowledge translation throughout the scientific enterprise rather than a single organizational unit. We use the established principles of Knowledge Discovery and Data Mining (KDD) to formalize a language around AI4OS. We then discuss three principle stages of knowledge translation embedded in AI4Science systems and detail specific points where openness can be applied to yield an AI4OS alternative. Lastly, we formulate a theoretical metric to assess AI4OS with a supporting ethical argument highlighting its importance. Our goal is that by drawing attention to AI4OS we can ensure the natural consequence of AI4Science (e.g., self-driving labs) is a benefit not only for its developers but for society as a whole.

翻译：人工智能驱动的科学研究（AI4Science），尤其是自动驾驶实验室的形式，有可能削弱人类参与并阻碍更广泛社区内的科学发现。尽管既有研究聚焦于确保人工智能应用的责任部署、增强安全性以及保证可解释性，我们同时提出应当审慎考虑提升AI4Science发现的开放性。本文提出“面向开放科学的人工智能”（AI4OS）概念，将其作为AI4Science的多智能体扩展，核心原则是在整个科学事业中（而非单一组织单元）最大化开放知识转化。我们采用知识发现与数据挖掘（KDD）的成熟原理来形式化描述AI4OS的语言体系。随后，我们探讨嵌入AI4Science系统中的知识转化的三个关键阶段，并详细阐述可引入开放性的具体环节，从而构建AI4OS的替代方案。最后，我们提出一个理论指标用于评估AI4OS，并辅以支持其重要性的伦理论证。我们的目标是，通过引起对AI4OS的关注，确保AI4Science（例如自动驾驶实验室）的自然成果不仅惠及开发者，更能造福整个社会。