To guard against this bias, adjustments are made to the data, labels, model training, scoring systems and other aspects of the machine learning system.
As the use of machine learning has increased in areas such as criminal justice, hiring, health care delivery and social service interventions, concerns have grown over whether such applications introduce new or amplify existing inequities, especially among racial minorities and people with economic disadvantages.
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