在黑盒子中的政治偏见的高风险情绪推断模型
Hubert Plisiecki1,2,3, Paweł Lenartowicz4, Maria Flakus5
1Institute of Psychology, Polish Academy of Sciences, Warsaw, Poland. hplisiecki@gmail.com.
Scientific reports
|February 19, 2025
概括
在波兰情绪分析 (SA) 模型中存在政治偏见,影响了文本的解释. 人类注释和培训数据引入了这种偏见,需要在研究中仔细使用SA.
科学领域:
- 计算语言学 计算语言学
- 社会科学研究方法 社会科学研究方法论
- 机器学习伦理学 机器学习伦理学
背景情况:
- 机器学习模型,包括情感分析 (SA),可以从训练数据中继承偏见.
- 之前的研究已经确定了性别和种族偏见,但政治偏见仍未得到充分研究.
- 政治偏见可以在学术和应用环境中显著扭曲文本数据的解释.
研究的目的:
- 在波兰情绪分析模型中调查和量化政治偏见.
- 在模型的开发管道中识别政治偏见的来源.
- 评估政治偏见对社会科学研究中SA有效性的影响.
主要方法:
- 审计一个定制的波兰情绪分析模型,以寻找政治偏见.
- 分析与波兰政治家相关的名字和句子的价值预测.
- 修剪培训数据集以评估减少偏差.
主要成果:
- 观察到价值预测的系统差异,与政治归属相关.
- 人类评价者注释被确定为传播政治偏见的关键来源.
- 数据集修剪减少了偏差,但没有消除它,表明了复杂的偏差起源.
结论:
- 政治偏见对社会科学研究中情绪分析的可靠性构成重大威胁.
- 在使用SA模型时建议谨慎使用;对结果的批判性检查是必不可少的.
- 基于词典的系统被提出为一种潜在的更具意识形态中立性的替代方案.
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