High-dimensional representations for words, text, images, knowledge graphs and other structured data are commonly used in different paradigms of machine learning and data mining. These representations have different degrees of interpretability, with efficient distributed representations coming at the cost of the loss of feature to dimension mapping. This implies that there is obfuscation in the way concepts are captured in these embedding spaces. Its effects are seen in many representations and tasks, one particularly problematic one being in language representations where the societal biases, learned from underlying data, are captured and occluded in unknown dimensions and subspaces. As a result, invalid associations (such as different races and their association with a polar notion of good versus bad) are made and propagated by the representations, leading to unfair outcomes in different tasks where they are used. This work addresses some of these problems pertaining to the transparency and interpretability of such representations. A primary focus is the detection, quantification, and mitigation of socially biased associations in language representation.

中文翻译：

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分布式表示的几何形状可实现更好的对齐，减小的偏差并提高可解释性

单词，文本，图像，知识图和其他结构化数据的高维表示形式通常用于机器学习和数据挖掘的不同范例中。这些表示具有不同程度的可解释性，有效的分布式表示会以要素到尺寸映射的损失为代价。这意味着在这些嵌入空间中捕获概念的方式存在混淆。它的效果在许多表示形式和任务中都可以看到，其中一种特别有问题的是在语言表示形式中，从基础数据中学到的社会偏见被捕获并封闭在未知的维度和子空间中。结果是，表示会产生并传播无效的关联（例如，不同的种族以及它们与优劣之极的关联），从而在使用它们的不同任务中导致不公平的结果。这项工作解决了与此类表示的透明度和可解释性有关的一些问题。主要重点是检测，量化和减轻语言表达中社会偏见的联想。