The choice of features recorded in the training data for machine learning is critical for both the accuracy and ethical integrity of subsequent machine learning. In particular, a poor choice of features may lead to combinations becoming proxy variables for protected characteristics such as gender or race, and thus give rise to bias in eventual ML model.
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