Semantics derived automatically from language corpora contain human-like biases
Semantics derived automatically from language corpora contain human-like biases
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DOI：
10.1126/science.aal4230
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发表时间：
2017-04-14
期刊：
影响因子：
56.9
通讯作者：
Narayanan, Arvind
中科院分区：
文献类型：
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作者：
Caliskan, Aylin;Bryson, Joanna J.;Narayanan, Arvind
机器学习是一种通过发现现有数据中的模式来获得人工智能的方法。在这里，我们证明了将机器学习应用于普通人类语言会导致类似人类的语义偏见。我们复制了一系列已知的偏见，通过内隐联想测试测量，使用广泛使用的纯统计机器学习模型，在万维网的标准文本语料库上训练。我们的研究结果表明，文本语料库包含可恢复的和准确的印记，我们的历史偏见，无论是道德中立的昆虫或花卉，有问题的种族或性别，甚至只是真实的，反映了现状的性别分布方面的职业或名字。我们的方法有望识别和解决文化中的偏见来源，包括技术。
Machine learning is a means to derive artificial intelligence by discovering patterns in existing data. Here, we show that applying machine learning to ordinary human language results in human-like semantic biases. We replicated a spectrum of known biases, as measured by the Implicit Association Test, using a widely used, purely statistical machine-learning model trained on a standard corpus of text from the World Wide Web. Our results indicate that text corpora contain recoverable and accurate imprints of our historic biases, whether morally neutral as toward insects or flowers, problematic as toward race or gender, or even simply veridical, reflecting the status quo distribution of gender with respect to careers or first names. Our methods hold promise for identifying and addressing sources of bias in culture, including technology.