Is there any gender/race bias in hep-lat primary publication? Machine-Learning Evaluation of Author Ethnicity and Gender

TL;DR

This study uses machine learning to analyze primary hep-lat publications, investigating potential race and gender biases in the publication process by predicting author ethnicity and gender from names and examining outcome disparities.

Contribution

It introduces a machine learning approach to assess race and gender bias in scientific publishing, providing measurable insights into publication disparities based on author demographics.

Findings

Identifies measurable differences in publication outcomes by gender and race.

Demonstrates machine learning effectiveness in predicting author demographics from names.

Highlights the need for journal and institutional policy improvements.

Abstract

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.