How can we ensure data transparency and sharing in academic research when using AI?

Ensuring data transparency and AI-enabled research sharing is feasible through structured frameworks promoting open science principles. This involves deliberate commitment to documenting and sharing data, methodologies, and AI tools responsibly. Key principles include adopting open licenses facilitating reuse (e.g., Creative Commons), employing FAIR (Findable, Accessible, Interoperable, Reusable) data practices, and transparently reporting AI model specifications, training data characteristics, computational environments, and hyperparameters. Necessary conditions encompass robust metadata standards, use of trusted repositories, and comprehensive documentation detailing data collection, curation, and AI development processes. Critical considerations involve data privacy and confidentiality, necessitating appropriate de-identification techniques (e.g., pseudonymization, synthetic data generation) and ethical review, particularly for sensitive human data. Implementation requires utilizing domain-specific repositories supporting large datasets and code (e.g., Zenodo, Figshare, OSF), applying detailed metadata schemas, and documenting data processing pipelines and AI model versioning meticulously. Preregistering analysis plans enhances transparency. This facilitates verification, reproducibility, independent validation, and reuse of findings, thereby strengthening research integrity, fostering collaboration, accelerating discovery, and building public trust in AI-assisted academic outcomes.