Gender Bias in Big Data Analysis

This article combines humanistic "data critique" with informed inspec-tion of big data analysis. It measures gender bias when gender prediction software tools (Gender API, Namsor, and Genderize.io) are used in historical big data research. Gender bias is measured by contrasting personally identified com-puter science authors in the well-regarded DBLP dataset (1950-80) with exactly comparable results from the software tools. Implications for public understanding of gender bias in comput-ing and the nature of the computing profession are outlined. Preliminary assessment of the Semantic Scholar dataset is presented. The conclu-sion combines humanistic approaches with selective use of big data methods.