一句话：博士论文计划识别开源社区中性别偏见的体现、程度与频率，并开发缓解策略与SMD文本识别工具。
当代软件开发组织由异性恋男性主导，缺乏多样性，女性与LGBTQ+等群体常遭遇偏见、性别歧视和厌女，导致部分女性转行。偏见因此成为促进多样性与包容性的障碍。该博士论文研究旨在通过识别各种性别偏见的体现、程度和频率，推动软件开发组织中的多样性与包容性。具体计划包括：i) 研究贡献者性别在FLOSS项目代码评审过程中的影响；ii) 研究不同维度性别偏见的频率及其影响；iii) 开发工具识别性别歧视、厌女及贬损（SMD）文本。摘要未报告已完成结果，属于研究计划。
Contemporary software development organizations are dominated by straight males and lack diversity. As a result, people from other demographic such as women and LGBTQ+ often encounter bias, sexism, and misogyny. Due to negative experiences, many women switch careers. Therefore, biases pose barriers to promote diversity and inclusion. To get benefits from diverse pools of talents and reduce the attrition rate of minorities, we need to identify the degree and effect of various biases and develop mitigation strategies. Therefore, my dissertation study aims at promoting diversity and inclusion among software development organizations by identifying the manifestation, magnitude, and frequency of various gender biases. For this purpose, I plan to investigate i) the effect of gender of the contributors in the code review process of Free/Libre Open Source Software (FLOSS) projects, ii) the frequency of different dimensions of gender bias and their effect, and iii) develop a tool to identify sexist and misogynistic and derogatory (SMD) texts.
DOI 原文 ·
@inproceedings{paperbot7280,
title = {Identification and Mitigation of Gender Biases to Promote Diversity and Inclusion among Open Source Communities},
author = {Sayma Sultana},
booktitle = {ASE},
year = {2022},
doi = {10.1145/3551349.3559571}
}