  This article combines humanistic "data critique" with informed inspection 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 computer
science authors in the well-regarded DBLP dataset (1950-1980) with exactly
comparable results from the software tools. Implications for public
understanding of gender bias in computing and the nature of the computing
profession are outlined. Preliminary assessment of the Semantic Scholar dataset
is presented. The conclusion combines humanistic approaches with selective use
of big data methods.
