  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, few
large-scale analyses have been performed on this topic, mostly because of the
lack of robust race-disambiguation algorithms. Identifying author information
does not generally include the author's race. Therefore, an algorithm needs to
be employed, using known information about authors, i.e., their names, to infer
their perceived race. Nevertheless, as any other algorithm, the process of
racial inference can generate biases if it is not carefully considered. When
the research is focused on the understanding of racial-based inequalities, such
biases undermine the objectives of the investigation and may perpetuate
inequities. The goal of this article is to assess the biases introduced by the
different approaches used name-based racial inference. We use information from
US census and mortgage applications to infer the race of US author names 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 and ethnicity and that
threshold approaches underestimate Black authors and overestimate White
authors. We conclude with recommendations to avoid potential biases. This
article fills an important research gap that will allow more systematic and
unbiased studies on racial disparity in science.
