  As Machine Learning models continue to be relied upon for making automated
decisions, the issue of model bias becomes more and more prevalent. In this
paper, we approach training a text classifica-tion model and optimize on bias
minimization by measuring not only the models performance on our dataset as a
whole, but also how it performs across different subgroups. This requires
measuring per-formance independently for different demographic subgroups and
measuring bias by comparing them to results from the rest of our data. We show
how unintended bias can be detected using these metrics and how removing bias
from a dataset completely can result in worse results.
