论文标题
DBIAS：发现偏见并确保新闻文章的公平性
Dbias: Detecting biases and ensuring Fairness in news articles
论文作者
论文摘要
由于以数据为中心的系统和算法在机器学习中的使用越来越多，公平的话题在学术和更广泛的文献中引起了很多关注。本文介绍了DBIAS（https://pypi.org/project/dbias/），这是一个开放式Python软件包，用于确保新闻文章中的公平性。 DBIAS可以采用任何文本来确定其是否有偏见。然后，它检测到文本中有偏见的单词，掩盖它们，并提出了一组句子，这些句子没有偏见或至少有偏见的新单词。我们进行广泛的实验以评估DBIA的性能。为了了解我们的方法的运转效果，我们将其与现有的公平模型进行比较。我们还测试了DBIAS的各个组件，以了解它们的有效性。实验结果表明，在准确性和公平性方面，DBIAS优于所有基准。我们将此软件包（DBIA）公开，以供开发人员和从业人员减轻文本数据（例如新闻文章）的偏见，并鼓励扩展这项工作。
Because of the increasing use of data-centric systems and algorithms in machine learning, the topic of fairness is receiving a lot of attention in the academic and broader literature. This paper introduces Dbias (https://pypi.org/project/Dbias/), an open-source Python package for ensuring fairness in news articles. Dbias can take any text to determine if it is biased. Then, it detects biased words in the text, masks them, and suggests a set of sentences with new words that are bias-free or at least less biased. We conduct extensive experiments to assess the performance of Dbias. To see how well our approach works, we compare it to the existing fairness models. We also test the individual components of Dbias to see how effective they are. The experimental results show that Dbias outperforms all the baselines in terms of accuracy and fairness. We make this package (Dbias) as publicly available for the developers and practitioners to mitigate biases in textual data (such as news articles), as well as to encourage extension of this work.
