论文标题
大数据分析中的性别偏见
Gender Bias in Big Data Analysis
论文作者
论文摘要
本文将人文主义的“数据批评”与大数据分析的知情检查结合在一起。当历史大数据研究中使用性别预测软件工具（性别API，NAMSOR和GENDERIZE.IO）时，它可以衡量性别偏见。性别偏见是通过将备受赞誉的DBLP数据集（1950-1980）中的个人识别的计算机科学作者与软件工具的可比结果进行了对比来衡量的。概述了公众对计算中性别偏见和计算行业性质的影响。介绍了语义学者数据集的初步评估。该结论将人文主义方法与选择性使用大数据方法相结合。
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.
