| Popis: |
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. |
