be useful to researchers in the development of new hypotheses.

$\hat{U}$ is also useful to understand models built on social media data. Until recently, the models themselves were not very important. However, machine learning can now be used to estimate sensitive traits such criminal recidivism [@kleinberg2016inherent]. Given the literalness with which estimates are often interpreted, it is essential to note that model weights are causal for the predicted label. In Section \[gender\_bias\] we use our understanding of the input features to characterize information the model extracts to predict religion. This dataset also includes demographic labels, which show predicted religion labels are more gendered than the ground truth.

We hope the included word lists (a) highlight unstudied relationships about these traits (b) illustrate what kind of information is extracted from social media by machine learning systems.

Results and Discussion
======================

Experimental Setup
------------------

All BoW experiments employ the same preprocessing. Users must have over 500 words in the sum of all their statuses. 80% of the data is randomly assigned to the training set; the remaining samples constitute the test set. The vocabulary is limited to the 40,000 most common words in each training set. Words must be used by at 