在协作平台上量化基于行为的性别歧视
Orsolya Vásárhelyi1,2,3, Balázs Vedres3,4,5
1Center for Collective Learning, Corvinus Institute for Advanced Studies, Corvinus University of Budapest, Fővám tér 8., Budapest 1093, Hungary.
PNAS nexus
|February 10, 2025
概括
性别行为,而不是性别本身,在GitHub和Behance等数字平台上造成不利影响. 女性因性别典型行为而面临惩罚,影响职业进步和成功.
科学领域:
- 社会科学 社会科学 社会科学
- 计算机科学 计算机科学
- 性别研究是性别研究.
背景情况:
- 数字协作平台为创意领域的职业发展提供了机会.
- 在这些平台上,性别歧视仍然存在,尤其影响到女性.
- 算法管理和人工智能系统影响用户的可见性和成功.
研究的目的:
- 在数字平台上分析基于行为的性别歧视.
- 量化性别典型行为对用户劣势的影响.
- 在GitHub和Behance上调查性别劣势.
主要方法:
- 在GitHub (软件开发) 和Behance (美术/设计) 上分析用户行为.
- 用户行为中的性别典型性的量化.
- 评估行为与平台成功指标 (注意力,生存) 之间的相关性.
主要成果:
- 性别典型的行为解释了60-90%的妇女的劣势.
- 男人和女人都因表现出高度女性化的行为而受到惩罚.
- 在确定劣势方面,绝对的性别不如行为模式那么重要.
结论:
- 数字平台上的行为刻板印象延续了性别劣势.
- 算法系统可以放大这些刻板印象的后果.
- 解决行为偏见对于在数字平台上取得公平进步至关重要.
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