  Racial disparity in academia is a widely acknowledged problem. The
quantitative understanding of racial based systemic inequalities is an
important step towards a more equitable research system. However, because of
the lack of robust information on authors' race, few large scale analyses have
been performed on this topic. Algorithmic approaches offer one solution, using
known information about authors, such as their names, to infer their perceived
race. As with any other algorithm, the process of racial inference can generate
biases if it is not carefully considered. The goal of this article is to assess
the extent to which algorithmic bias is introduced using different approaches
for name based racial inference. We use information from the U.S. Census and
mortgage applications to infer the race of U.S. affiliated authors in the Web
of Science. We estimate the effects of using given and family names, thresholds
or continuous distributions, and imputation. Our results demonstrate that the
validity of name based inference varies by race/ethnicity and that threshold
approaches underestimate Black authors and overestimate White authors. We
conclude with recommendations to avoid potential biases. This article lays the
foundation for more systematic and less biased investigations into racial
disparities in science.
