论文标题
Grep-Biasir：用于调查信息检索结果中性别代表性偏见的数据集
Grep-BiasIR: A Dataset for Investigating Gender Representation-Bias in Information Retrieval Results
论文作者
论文摘要
信息检索（IR）系统提供的内容可以反映现有的社会偏见和刻板印象。检索结果中的这种偏见可能会导致社会和系统中的进一步建立和加强刻板印象。为了促进IR系统检索结果中性别偏见的研究，我们引入了信息检索的性别表示偏差（GREP-BIASIR），这是一个新型的彻底审核的数据集，该数据集由118个偏见敏感的中性搜索查询组成。一组查询涵盖了广泛的与性别相关的主题，为此，搜索结果中性别的偏见可以被视为社会问题。每个查询都配有一个相关和一个非相关文件，其中该文件也以三种变体的女性，男性和中性提供。该数据集可在https://github.com/klarakrieg/grepbiasir上找到。
The provided contents by information retrieval (IR) systems can reflect the existing societal biases and stereotypes. Such biases in retrieval results can lead to further establishing and strengthening stereotypes in society and also in the systems. To facilitate the studies of gender bias in the retrieval results of IR systems, we introduce Gender Representation-Bias for Information Retrieval (Grep-BiasIR), a novel thoroughly-audited dataset consisting of 118 bias-sensitive neutral search queries. The set of queries covers a wide range of gender-related topics, for which a biased representation of genders in the search result can be considered as socially problematic. Each query is accompanied with one relevant and one non-relevant document, where the document is also provided in three variations of female, male, and neutral. The dataset is available at https://github.com/KlaraKrieg/GrepBiasIR.
