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High-dimensional representations for words, text, images, knowledge graphsand other structured data are commonly used in different paradigms of machinelearning and data mining. These representations have different degrees ofinterpretability, with efficient distributed representations coming at the costof the loss of feature to dimension mapping. This implies that there isobfuscation in the way concepts are captured in these embedding spaces. Itseffects are seen in many representations and tasks, one particularlyproblematic one being in language representations where the societal biases,learned from underlying data, are captured and occluded in unknown dimensionsand subspaces. As a result, invalid associations (such as different races andtheir association with a polar notion of good versus bad) are made andpropagated by the representations, leading to unfair outcomes in differenttasks where they are used. This work addresses some of these problemspertaining to the transparency and interpretability of such representations. Aprimary focus is the detection, quantification, and mitigation of sociallybiased associations in language representation.

The Geometry of Distributed Representations for Better Alignment, Attenuated Bias, and Improved Interpretability