Fairness in Natural Language Processing

近年来，自然语言处理(NLP)取得了令人瞩目的商业成功，并被部署在越来越广泛的设备和应用领域。与此同时，有明显的证据表明，训练有素的模型放大了训练数据中的偏见，并且在与不同人口统计群体相关的文本中表现不一致。这个项目旨在系统地量化这种偏见的程度，并开发既更加社会公平，又不太容易在学习的陈述中暴露私人数据的模型。通过这样做，它将使NLP更容易为新的用户群体所接受，并消除NLP采用的社会技术障碍。

Natural language processing (NLP) has achieved spectacular commercial successes in recent years, and has been deployed across an ever-increasing breadth of devices and application areas. At the same time, there has been stark evidence to indicate that naively-trained models amplify biases in training data, and perform inconsistently across text relating to different demographic groupings of individuals. This project aims to systematically quantify the extent of such biases, and develop models that are both more socially equitable, as well as less prone to expose private data in the learned representations. In doing so, it will make NLP more accessible to new populations of users, and remove socio-technological barriers to NLP uptake.