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AI for Science (AI4Science), particularly in the form of self-driving labs,has the potential to sideline human involvement and hinder scientific discoverywithin the broader community. While prior research has focused on ensuring theresponsible deployment of AI applications, enhancing security, and ensuringinterpretability, we also propose that promoting openness in AI4Sciencediscoveries should be carefully considered. In this paper, we introduce theconcept of AI for Open Science (AI4OS) as a multi-agent extension of AI4Sciencewith the core principle of maximizing open knowledge translation throughout thescientific enterprise rather than a single organizational unit. We use theestablished principles of Knowledge Discovery and Data Mining (KDD) toformalize a language around AI4OS. We then discuss three principle stages ofknowledge translation embedded in AI4Science systems and detail specific pointswhere openness can be applied to yield an AI4OS alternative. Lastly, weformulate a theoretical metric to assess AI4OS with a supporting ethicalargument highlighting its importance. Our goal is that by drawing attention toAI4OS we can ensure the natural consequence of AI4Science (e.g., self-drivinglabs) is a benefit not only for its developers but for society as a whole.

AI for Open Science: A Multi-Agent Perspective for Ethically Translating Data to Knowledge