Fairness in Artificial Intelligence: Regulatory Sanbox Evaluation of Bias Prevention for ECG Classification

As the use of artificial intelligence within healthcare is on the rise, an increased attention has been directed towards ethical considerations. Defining fairness in machine learning is a well explored topic with an extensive literature. However, such definitions often rely on the existence of metrics on the input data and well-defined outcome measurements, while regulatory definitions use general terminology. This work aims to study fairness within AI, particularly bringing regulation and theoretical knowledge closer. The study is done via a regulatory sandbox implemented on a healthcare case, specifically ECG classification.