Temporal Analysis and Gender Bias in Computing

TL;DR

This paper analyzes historical gender bias in computing by examining name-based gender shifts over time, revealing biases in data and challenging assumptions about gender representation in early computer science.

Contribution

It introduces a systematic method to assess gender shifts in names over decades, highlighting biases in historical gender analysis in computing.

Findings

Identified 300 names with significant gender shifts from 1925 to 1975.

Quantified a net 'female shift' leading to potential overcounting of women.

Challenged the view of programming as predominantly masculine during early decades.

Abstract

Recent studies of gender bias in computing use large datasets involving automatic predictions of gender to analyze computing publications, conferences, and other key populations. Gender bias is partly defined by software-driven algorithmic analysis, but widely used gender prediction tools can result in unacknowledged gender bias when used for historical research. Many names change ascribed gender over decades: the "Leslie problem." Systematic analysis of the Social Security Administration dataset -- each year, all given names, identified by ascribed gender and frequency of use -- in 1900, 1925, 1950, 1975, and 2000 permits a rigorous assessment of the "Leslie problem." This article identifies 300 given names with measurable "gender shifts" across 1925-1975, spotlighting the 50 given names with the largest such shifts. This article demonstrates, quantitatively, there is net "female shift" that likely results in the overcounting of women (and undercounting of men) in earlier decades, just as computer science was professionalizing. Some aspects of the widely accepted 'making programming masculine' perspective may need revision.