AI Research is not Magic, it has to be Reproducible and Responsible: Challenges in the AI field from the Perspective of its PhD Students

Unlocking the full societal potential of artificial intelligence demands a fundamental shift towards responsible and reproducible research. Understanding that PhD students are pivotal in conducting and reproducing experiments, we investigated the challenges of 28 AI PhD candidates from 13 European countries. We identify three critical areas where current practices fall short: (1) the findability and quality of AI resources such as datasets, models, and experiments; (2) the difficulties in replicating the experiments in AI papers; (3) and the lack of trustworthiness and interdisciplinarity. After uncovering some of the underlying reasons behind the challenges, we propose a combination of social and technical recommendations to overcome the identified challenges and foster a more transparent and reliable AI research ecosystem. Socially, we recommend the general adoption of reproducibility initiatives in AI conferences and journals, as well as improved interdisciplinary scientific collaboration, especially in data governance practices. On the technical front, we call for enhanced tools to better support versioning control of datasets and code, and a computing infrastructure that facilitates the sharing and discovery of AI resources, as well as the sharing, execution, and verification of experiments.