AI for Open Science: A Multi-Agent Perspective for Ethically Translating Data to Knowledge
This addresses the problem of ensuring AI-driven scientific advancements benefit society broadly rather than just developers, though it is incremental as it builds on existing AI4Science frameworks.
The paper tackles the risk that AI for Science (AI4Science) may sideline human involvement and hinder community discovery by proposing AI for Open Science (AI4OS) as a multi-agent extension to maximize open knowledge translation, formalizing it with Knowledge Discovery and Data Mining principles and developing a theoretical metric with ethical arguments.
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.