This article presents an extensive literature review on the importance of fairness in society, science, the world of work and leisure, with a focus on healthcare. Depending on the application area, fairness criteria and metrics play a major role in evaluation, classification, and allocation. Different approaches to a general definition of algorithmic fairness for individuals or groups are considered, and their measures from the perspective of the concerned sciences and requirements for the decision-making processes are also formulated. There are many reasons for the lack of fairness: inadequate data quality or low model performance, differences in understanding, competing standards, inappropriate measures in selection, classification and decision-making, lack of accuracy or performance of algorithms paired with insufficient communication, interaction or collaboration of stakeholders. The requirements are illustrated using the example of medical risk prediction tools, e.g., the individual and familial risk for the occurrence of pathogenic variants in BRCA1 (BReast CAncer 1) or BRCA2 genes with impact on early breast cancer (BC) and ovarian cancer (OC) disease, and the 5-year risk that an individual with ocular hypertension will develop Primary Open Angle Glaucoma (POAG), the leading global cause of irreversible blindness.  

Trust, (algorithmic) fairness, assessment, discrimination, measure, risk prevention, medical tool, explainable artificial intelligence