Search Engines (SE) have been shown to perpetuate well-known gender stereotypes identified in psychology literature and to influence users accordingly. Similar biases were found encoded in Word Embeddings (WEs) learned from large online corpora. In this context, we propose the Gender Stereotype Reinforcement (GSR) measure, which quantifies the tendency of a SE to support gender stereotypes, leveraging gender-related information encoded in WEs.

Through the critical lens of construct validity, we validate the proposed measure on synthetic and real collections. Subsequently, we use GSR to compare widely-used Information Retrieval ranking algorithms, including lexical, semantic, and neural models. We check if and how ranking algorithms based on WEs inherit the biases of the underlying embeddings. We also consider the most common debiasing approaches for WEs proposed in the literature and test their impact in terms of GSR and common performance measures. To the best of our knowledge, GSR is the first specifically tailored measure for IR, capable of quantifying representational harms.

搜索引擎（SE）已被证明可以使心理学文献中确定的众所周知的性别刻板印象永久存在，并相应地影响用户。从大型在线语料库学到的词嵌入（WE）中也发现了类似的偏见。在这种情况下，我们提出了“增强性别刻板印象”（GSR）措施，该策略量化了SE支持WEB编码的性别相关信息的性别刻板印象的趋势。

通过构建有效性的关键镜头，我们验证了拟议的综合和真实馆藏措施。随后，我们使用GSR来比较广泛使用的信息检索排名算法，包括词汇，语义和神经模型。我们检查基于WE的排名算法是否以及如何继承基础嵌入的偏差。我们还考虑了文献中提出的最常见的WEs去偏方法，并测试了它们在GSR和常见绩效指标方面的影响。据我们所知，GSR是第一个专门针对IR量身定制的措施，能够量化代表损害。