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Machine learning models are increasingly adopted for facilitating clinical decision-making. However, recent research has shown that machine learning techniques may result in potential biases when making decisions for people in different subgroups, which can lead to detrimental effects on the health and well-being of vulnerable groups such as ethnic minorities. This problem, termed algorithmic bias, has been extensively studied in theoretical machine learning recently. However, how it willdoi:10.1101/2022.01.16.21267299 fatcat:26fp56upvfgabozuqowrrun3j4