A Transparent Framework for Evaluating Unintended Demographic Bias in Word Embeddings
A Transparent Framework for Evaluating Unintended Demographic Bias in Word Embeddings
复制标题
用于评估词嵌入中意外人口统计偏差的透明框架
DOI：
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复制
发表时间：
2019
期刊：
影响因子：
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通讯作者：
M. Najafian
中科院分区：
文献类型：
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作者：
Chris Sweeney;M. Najafian
词嵌入模型在自然语言处理社区中获得了很大的吸引力，然而，它们受到了意想不到的人口统计偏见的影响。大多数评估这些偏差的方法都依赖于基于向量空间的度量，如词嵌入关联测试（WEAT）。虽然这些方法为嵌入向量空间中的非预期偏差提供了很好的几何见解，但它们未能为嵌入如何在下游NLP应用中引起歧视提供可解释的意义。在这项工作中，我们提出了一个透明的框架和指标，用于评估受保护群体之间的歧视与他们的词嵌入偏见。我们的指标（相对消极情绪偏差，RNSB）通过与来自各种受保护群体的人口统计学身份术语相关的相对消极情绪来衡量词嵌入的公平性。我们表明，我们的框架和指标，使有用的分析到词嵌入的偏见。
Word embedding models have gained a lot of traction in the Natural Language Processing community, however, they suffer from unintended demographic biases. Most approaches to evaluate these biases rely on vector space based metrics like the Word Embedding Association Test (WEAT). While these approaches offer great geometric insights into unintended biases in the embedding vector space, they fail to offer an interpretable meaning for how the embeddings could cause discrimination in downstream NLP applications. In this work, we present a transparent framework and metric for evaluating discrimination across protected groups with respect to their word embedding bias. Our metric (Relative Negative Sentiment Bias, RNSB) measures fairness in word embeddings via the relative negative sentiment associated with demographic identity terms from various protected groups. We show that our framework and metric enable useful analysis into the bias in word embeddings.
DOI：
10.1073/pnas.1720347115
发表时间：
2018-04-17
影响因子：
11.1
作者：
Garg, Nikhil;Schiebinger, Londa;Zou, James
通讯作者：
Zou, James