Translating Algorithmic Fairness into Health Practice: Formative Evaluation of the Fairness-to-Action Framework
Published in Frontiers in Artificial Intelligence, 2026
Algorithmic fairness is increasingly acknowledged as a critical concern in digital health; however, existing knowledge on fairness remains challenging to operationalize and systematically embed into routine practice. Our study examines the knowledge-practice gap through a formative evaluation of the Fairness-to-Action framework and the development of a guidance artifact.
BibTex citation:
@ARTICLE{10.3389/frai.2026.1911214,
AUTHOR={Altamirano, Sara and Anadria, Daniel and van der Wees, Stefanie and Tensen, Paulien and van de Vijver, Steven and Ghebreab, Sennay },
TITLE={Translating algorithmic fairness into health practice: formative evaluation of the fairness-to-action framework},
JOURNAL={Frontiers in Artificial Intelligence},
VOLUME={Volume 9 - 2026},
YEAR={2026},
URL={https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2026.1911214},
DOI={10.3389/frai.2026.1911214},
ISSN={2624-8212} }}