We explore the role of Open Science & Open Data in shaping responsible AI. In times of rapidly advancing generative AI, principles like transparency, accessibility, and collaboration are more essential than ever. The open exchange of datasets, methods, and results supports innovation, reproducibility, and fairness—ultimately leading to more trustworthy AI. The summer school includes keynotes by renowned researchers, in-depth lectures, interactive hands-on sessions, and an excursion. Participants will also have time for discussions, exchange, and networking.

In this track, early career researchers who are Open Science novices learn to make their research more transparent, reproducible and credible in the eyes of their peers, the public, and funding agencies. Participants of this track learn how to: Specify research design and set up statistical plans in advance of collecting data to prevent biases in analyses, with the help of preregistration and data simulation; Create computationally reproducible workflows to be more efficient and spot mistakes in data wrangling or analyses, through programming with version controlled scripts; Prepare and share data and code in connection with manuscripts by applying the Findable, Accessible, Interoperable, Reusable principles, and using adequate repositories and licenses to accumulate credit and citations.