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

arXiv: https://arxiv.org/abs/2310.18852 · HTML: https://mlsanity.com/paper/2310.18852/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 40 references that are in the map.
- First posted on arXiv 28 Oct 2023.

## Builds on (most cited first, 6 of 6)

- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program)](https://mlsanity.com/paper/2003.12206/) (2020)
- [The Ethics of AI Ethics -- An Evaluation of Guidelines](https://mlsanity.com/paper/1903.03425/) (2019)
- [Responsible Autonomy](https://mlsanity.com/paper/1706.02513/) (2017)
- [DataHub: Collaborative Data Science & Dataset Version Management at Scale](https://mlsanity.com/paper/1409.0798/) (2014)
- [AI virtues -- The missing link in putting AI ethics into practice](https://mlsanity.com/paper/2011.12750/) (2020)

## Built on by (most cited first, 1 of 1)

- [CycleResearcher: Improving Automated Research via Automated Review](https://mlsanity.com/paper/2411.00816/) (2024)

## Abstract (arXiv)

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.