Comparison and benchmark of name-to-gender inference services

By Lucía Santamaría, Helena Mihaljević. PeerJ Computer Science, 2018

367 citations recorded by the source corpus.

The increased interest in analyzing and explaining gender inequalities in tech, media, and academia highlights the need for accurate inference methods to predict a person’s gender from their name. Several such services exist that provide access to large databases of names, often enriched with information from social media profiles, culture-specific rules, and insights from sociolinguistics. We compare and benchmark five name-to-gender inference services by applying them to the classification of a test data set consisting of 7,076 manually labeled names. The compiled names are analyzed and characterized according to their geographical and cultural origin. We define a series of performance metrics to quantify various types of classification errors, and define a parameter tuning procedure to search for optimal values of the services’ free parameters. Finally, we perform benchmarks of all services under study regarding several scenarios where a particular metric is to be optimized.

Subfields: Artificial Intelligence, Gender Studies, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/5fb2a59fea61d3e3ac8a183c1beb1478ca7a62cd