Work

Dataset documentation for responsible AI: analysis of suitability and usage for health datasets

Year: 2026

Type: article

Abstract: Artificial Intelligence (AI) is rapidly transforming healthcare, but also raising concerns about algorithmic biases that mostly stem from the training data. It is widely supported that transparent dataset documentation is key to enabling responsible AI development. Several standardized dataset documentation approaches have been established, such as Datasheet, Dataset Nutrition Label, Accountability Documentation, Healthsheet, and Data Card. However, their suitability and usage for health datasets remain unclear. In this Analysis, we compared all five approaches and evaluated their alignment with the STANDING Together Recommendations for Documentation of Health Datasets. We also investigated their real-world usage and gathered insights from generators and consumers of health datasets. Our findings reveal that none of these documentation approaches are used widely or fully suited for health datasets. We recommend developing a standard documentation approach for health datasets along with clear guidelines and automation tools to support adoption.

Source: npj Digital Medicine

DOI: 10.1038/s41746-026-02714-2

Authors: Anna Heinke, Lingling Huang, Kyongmi U. Simpkins, Fritz Gerald P. Kalaw, Apoorva Karsolia, Kiratjit Singh, Sanjay Soundarajan, Benjamin Panny, Camille Nebeker, Sally L. Baxter, Cecilia S. Lee, Aaron Y. Lee, Bhavesh Patel, Kadija S. Ferryman, Aydan Gasimova, Christopher G. Chute, Jessica Mitchell, Monique S. Bangudi, Abigail Lucero, Sara J. Singer, Amir Bahmani, Hanna Pittock, Arash Alavi, Gerald McGwin, Dawn S. Matthies, Virginia R. de Sa, Samantha Hurst, Brittany York, Nicholas Evans, Nayoon Gim, Julia P. Owen, Jamie Shaffer, Yi-Ju Chen, Colleen Cuddy, Shahin Hallaj, Cynthia Owsley, Sigfried Gold, Tao Wang, Ryan A. Nayebi, Trym Drag-Erlandsen, Majid Rodger