Catch Me If You Can: The Dynamic Nature of Bias in Machine Learning Applications
ABSTRACT Bias in machine learning (ML) applications represents systematic differences between expected and actual values of the predicted outputs, such that certain individuals or groups are systematically and disproportionately (dis)advantaged. This paper investigates the dynamic nature of bias in ML applications.
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