Translating algorithmic fairness into health practice: formative evaluation of the fairness-to-action framework

Introduction

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.

Methods