Identifying and Correcting Label Bias in Machine Learning

arXiv: Learning, Volume abs/1901.04966, 2019.

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Abstract:

Datasets often contain biases which unfairly disadvantage certain groups, and classifiers trained on such datasets can inherit these biases. In this paper, we provide a mathematical formulation of how this bias can arise. We do so by assuming the existence of underlying, unknown, and unbiased labels which are overwritten by an agent who i...More

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