  In this work, we analyze papers that are classified as primary hep-lat to
study whether there is any race or gender bias in the journal-publication
process. We implement machine learning to predict the race and gender of
authors based on their names and look for measurable differences between
publication outcomes based on author classification. We would like to invite
discussion on how journals can make improvements in their editorial process and
how institutions or grant offices should account for these publication
differences in gender and race.
