# Gender Bias in Big Data Analysis

arXiv: https://arxiv.org/abs/2211.09865 · HTML: https://mlsanity.com/paper/2211.09865/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2022.

## Built on by (most cited first, 1 of 1)

- [An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts](https://mlsanity.com/paper/2501.12521/) (2025)

## Abstract (arXiv)

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.