Reproducibility is at the core of solid scientific and technical research. The ability to repeat the research that is produced is a key approach for confirming the validity of a new scientific discovery. Ensuring the robustness and trustworthiness of science that is done using computing and data is critical. The inability to reproduce the results of an experiment can lead to a range of consequences, including the retraction of research at the conference / journal that published it. Furthermore, fostering reproducibility facilitates dissemination, comparison, and adoption of research. Therefore, it can also contribute to increasing the impact and enhancing the visibility of articles.

The European Project AI4Europe aims at building a shared computing and data infrastructure that will provide AI researchers, educators, and students across scientific fields and disciplines with access to compute resources and data, along with appropriate educational tools. Inside the project, the AI4Europe Reproducibility Initiative focuses on promoting reproducibility in the field of artificial intelligence (AI) by establishing specific standards and practices. This tutorial provides an overview of the project's mission and the significance of reproducibility in AI research. By ensuring that AI findings can be replicated and validated, reproducibility enhances trustworthiness and facilitates advancements in the field. The tutorial also explores existing reproducibility initiatives and introduces the AI4Europe Reproducibility Initiative, which incorporates standardized documentation, a federated computing infrastructure, and dedicated reproducibility tools to support reproducible research in AI