Word embeddings provide quantitative representations of word semantics and the associations between word meanings in text data, including in large repositories in media and social media archives. This article introduces social psychologists to word embedding research via a consideration of bias analysis, a topic of central concern in the discipline. We explain how word embeddings are constructed and how they can be used to measure bias along bipolar dimensions that are comparable to semantic differential scales. We review recent studies that show how familiar social biases can be detected in embeddings and how these change over time and in conjunction with real-world discriminatory practices. The evidence suggests that embeddings yield valid and reliable estimates of bias and that they can identify subtle biases that may not be communicated explicitly. We argue that word embedding research can extend scholarship on prejudice and stereotyping, providing measures of the bias environment of human thought and action.

中文翻译：

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使用词嵌入调查文化偏见

词嵌入提供了词语义的定量表示以及文本数据中词义之间的关联，包括媒体和社交媒体档案中的大型存储库。本文通过考虑偏差分析向社会心理学家介绍词嵌入研究，偏差分析是该学科的核心关注主题。我们解释了词嵌入是如何构建的，以及它们如何用于测量与语义差异量表相当的双极维度的偏差。我们回顾了最近的研究，这些研究表明如何在嵌入中检测到熟悉的社会偏见，以及这些偏见如何随着时间的推移以及与现实世界的歧视性做法相结合而发生变化。证据表明，嵌入可产生有效且可靠的偏差估计，并且它们可以识别可能未明确传达的细微偏差。我们认为词嵌入研究可以扩展偏见和刻板印象的学术研究，提供人类思想和行为的偏见环境的衡量标准。