This session includes two short keynote speeches and a panel discussion on the topic of “AI with and for Open Science”. It tackles this from different views, Ethics – Algorithms – Infrastructure, with the aim to see how AI supports researchers in their scientific discovery and what are the key ingridients for open infrastructures to make this happen. One of the primary ways AI is changing academia is through data analysis. Researchers can leverage AI algorithms to analyze vast amounts of data quickly and efficiently. This enables them to identify patterns, correlations, and trends that may not be easily discernible through traditional methods. Moreover, AI tools are being used to generate content, write code, resolve accessibility issues, reconfigure writing processes and detect plagiarism. All this is reshaping researcher practice and culture in how they communicate, how they share, how they view infrastructure. This session tackles the “AI with and for Open Science” topic from three views, Ethics – Algorithms – Infrastructure: Ethics in AI - principles and frameworks that put ethics and responsibility into practice in data analytics; dilemmas and challenges posed by work in AI and Data Science in the context of being transparent and accountable. Large Language Models (LLMs) – controlling the future of (open) access to science; how Generative AI tools may influence ways scientific output is accessed and legitimized; challenges and opportunities in developing and hosting these models and services. Open Infrastructure fit for LLM – having open-source text generation models and variations of them is a good thing as it enables research communities to adapt models to their domains faster, and to cut costs. How do we achieve this?