Inferring gender from name: a large scale performance evaluation study
Kriste Krstovski Yao Lu Ye Xu
A person's gender is a crucial piece of information when performing research
across a wide range of scientific disciplines, such as medicine, sociology,
political science, and economics, to name a few. However, in increasing
instances, especially given the proliferation of big data, gender information
is not readily available. In such cases researchers need to infer gender from
readily available information, primarily from persons' names. While inferring
gender from name may raise some ethical questions, the lack of viable
alternatives means that researchers have to resort to such approaches when the
goal justifies the means - in the majority of such studies the goal is to
examine patterns and determinants of gender disparities. The necessity of
name-to-gender inference has generated an ever-growing domain of algorithmic
approaches and software products. These approaches have been used throughout
the world in academia, industry, governmental and non-governmental
organizations. Nevertheless, the existing approaches have yet to be
systematically evaluated and compared, making it challenging to determine the
optimal approach for future research. In this work, we conducted a large scale
performance evaluation of existing approaches for name-to-gender inference.
Analysis are performed using a variety of large annotated datasets of names. We
further propose two new hybrid approaches that achieve better performance than
any single existing approach.