  Small business classification is a difficult and important task within many
applications, including customer segmentation. Training on small business names
introduces gender and geographic origin biases. A model for predicting one of
66 business types based only upon the business name was developed in this work
(top-1 f1-score = 60.2%). Two approaches to removing the bias from this model
are explored: replacing given names with a placeholder token, and augmenting
the training data with gender-swapped examples. The results for these
approaches is reported, and the bias in the model was reduced by hiding given
names from the model. However, bias reduction was accomplished at the expense
of classification performance (top-1 f1-score = 56.6%). Augmentation of the
training data with gender-swapping samples proved less effective at bias
reduction than the name hiding approach on the evaluated dataset.
