Gender Bias in Big Data Analysis

IF 0.6 4区 管理学 Q1 HISTORY
T. Misa
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引用次数: 1

Abstract

abstract: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 (1950–80) 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.
大数据分析中的性别偏见
摘要:本文将人文主义的“数据批判”与大数据分析的知情检验相结合。在历史大数据研究中使用性别预测软件工具(gender API、Namsor和Genderize.io)时,它测量性别偏见。性别偏见是通过将著名的DBLP数据集(1950-80)中的个人计算机科学作者与软件工具的完全可比结果进行对比来衡量的。概述了公众对计算机性别偏见和计算机职业性质的理解的意义。对语义学者数据集进行了初步评估。结论将人文主义方法与大数据方法的选择性使用相结合。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
0.80
自引率
16.70%
发文量
18
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