When using AI, how can we ensure that the research it helps is reproducible?

To ensure reproducibility in AI-assisted research, researchers must implement robust documentation practices and transparent workflows throughout the experimental process. Verifiable replication of results requires meticulous attention to methodological detail when employing artificial intelligence tools. Reproducibility hinges on several critical factors: comprehensively documenting all training data sources, preprocessing steps, and data manipulation procedures; providing exact software versions, libraries, and hardware specifications; permanently archiving both the original and processed data; sharing fully reproducible code, including detailed configuration files and scripts; specifying exact prompts, parameters, and seeds used with generative AI models; and disclosing inherent AI model limitations, stochasticity, and potential biases transparently. Open-source frameworks, containerization technologies, and established computational notebooks facilitate this by enabling executable code sharing alongside detailed provenance records. Implementing reproducibility involves integrating version control systems like Git for code and utilizing repositories such as Zenodo or Figshare for datasets, source code, trained model weights, and comprehensive README files documenting procedures explicitly. Employing tools for experiment tracking and environment replication, such as DVC or container platforms like Docker, further standardizes workflows. Researchers should also detail prompt engineering iterations and employ checksum verification for all digital artifacts. Crucially, peer-reviewed dissemination should include sufficient supplementary materials to independently verify AI processes and outputs.