论文标题
测量基础视觉和语言嵌入中的社会偏见
Measuring Social Biases in Grounded Vision and Language Embeddings
论文作者
论文摘要
我们将社会偏见的概念从语言嵌入到基础视力和语言嵌入。偏见存在于接地嵌入中，实际上似乎比未接地的嵌入更重要。尽管这一事实是，视觉和语言可能会遭受不同的偏见，人们可能希望这可以减轻两者的偏见。存在多种方式来概括测量单词嵌入偏差到这种新环境的指标。我们介绍了概括的空间（扎根和接地座位），并证明了三个概括回答了有关偏见，语言和视力如何相互作用的不同但重要的问题。这些指标用于新的数据集，这是第一个用于接地偏差的指标，该数据集是通过扩展标准语言偏置基准，并使用可可，概念标题和Google图像的10,228张图像来创建的。数据集构建具有挑战性，因为视觉数据集本身非常有偏见。这些偏见在系统中的存在将开始在部署时会产生现实世界的后果，从而仔细测量偏见，然后减轻建立公平的社会至关重要的偏见。
We generalize the notion of social biases from language embeddings to grounded vision and language embeddings. Biases are present in grounded embeddings, and indeed seem to be equally or more significant than for ungrounded embeddings. This is despite the fact that vision and language can suffer from different biases, which one might hope could attenuate the biases in both. Multiple ways exist to generalize metrics measuring bias in word embeddings to this new setting. We introduce the space of generalizations (Grounded-WEAT and Grounded-SEAT) and demonstrate that three generalizations answer different yet important questions about how biases, language, and vision interact. These metrics are used on a new dataset, the first for grounded bias, created by augmenting extending standard linguistic bias benchmarks with 10,228 images from COCO, Conceptual Captions, and Google Images. Dataset construction is challenging because vision datasets are themselves very biased. The presence of these biases in systems will begin to have real-world consequences as they are deployed, making carefully measuring bias and then mitigating it critical to building a fair society.
