Web: http://arxiv.org/abs/2201.11358

Jan. 28, 2022, 2:11 a.m. | Carlos Mougan, Jose M. Alvarez, Gourab K Patro, Salvatore Ruggieri, Steffen Staab

cs.LG updates on arXiv.org arxiv.org

Protected attributes are often presented as categorical features that need to
be encoded before feeding them into a machine learning algorithm. Encoding
these attributes is paramount as they determine the way the algorithm will
learn from the data. Categorical feature encoding has a direct impact on the
model performance and fairness. In this work, we compare the accuracy and
fairness implications of the two most well-known encoders: one-hot encoding and
target encoding. We distinguish between two types of induced bias …

arxiv fairness

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