论文标题
通过机器学习，在政客的媒体报道中揭示性别偏见
Uncovering Gender Bias in Media Coverage of Politicians with Machine Learning
论文作者
论文摘要
本文介绍了研究在媒体中使用人工智能在媒体中代表政治领导人的代表时发现系统的性别偏见的研究。收集了15年的报纸对爱尔兰部长的报道，并通过自然语言处理技术和机器学习进行了分析。调查结果证明了在女性政客的描绘中，性别偏见的证据，与他们作为政治领导人的绩效评估的政策以及如何评估。本文还提出了一种方法，即可以在性别理论和女权主义语言学建立的理论框架内利用人工智能的技术对媒体内容进行大规模分析。
This paper presents research uncovering systematic gender bias in the representation of political leaders in the media, using artificial intelligence. Newspaper coverage of Irish ministers over a fifteen year period was gathered and analysed with natural language processing techniques and machine learning. Findings demonstrate evidence of gender bias in the portrayal of female politicians, the kind of policies they were associated with and how they were evaluated in terms of their performance as political leaders. This paper also sets out a methodology whereby media content may be analysed on a large scale utilising techniques from artificial intelligence within a theoretical framework founded in gender theory and feminist linguistics.
