论文标题
确保新闻文章公平性的方法
An Approach to Ensure Fairness in News Articles
论文作者
论文摘要
推荐系统，信息检索和其他信息访问系统提出了在非结构化文本中检查和应用公平和偏见缓解概念的独特挑战。本文介绍了DBIAS，这是一个Python包，可确保新闻文章的公平性。 DBIAS是一种受过训练的机器学习（ML）管道，可以使用文本（例如，段落或新闻故事），并检测文本是否有偏见。然后，它检测到文本中的有偏见的单词，掩盖它们，并推荐一组句子，这些句子没有偏见或至少偏见的新单词。我们结合了数据科学最佳实践的要素，以确保该管道可再现和可用。我们在实验中表明，该管道可以有效缓解偏见，并优于确保新闻文章公平性的常见神经网络体系结构。
Recommender systems, information retrieval, and other information access systems present unique challenges for examining and applying concepts of fairness and bias mitigation in unstructured text. This paper introduces Dbias, which is a Python package to ensure fairness in news articles. Dbias is a trained Machine Learning (ML) pipeline that can take a text (e.g., a paragraph or news story) and detects if the text is biased or not. Then, it detects the biased words in the text, masks them, and recommends a set of sentences with new words that are bias-free or at least less biased. We incorporate the elements of data science best practices to ensure that this pipeline is reproducible and usable. We show in experiments that this pipeline can be effective for mitigating biases and outperforms the common neural network architectures in ensuring fairness in the news articles.
