• Corpus ID: 233168618

VERB: Visualizing and Interpreting Bias Mitigation Techniques for Word Representations

@article{Rathore2021VERBVA,
  title={VERB: Visualizing and Interpreting Bias Mitigation Techniques for Word Representations},
  author={Archit Rathore and Sunipa Dev and J. M. Phillips and Vivek Srikumar and Yan-luan Zheng and Chin-Chia Michael Yeh and Junpeng Wang and Wei Zhang and Bei Wang},
  journal={ArXiv},
  year={2021},
  volume={abs/2104.02797}
}
Word vector embeddings have been shown to contain and amplify biases in data they are extracted from. Consequently, many techniques have been proposed to identify, mitigate, and attenuate these biases in word representations. In this paper, we utilize interactive visualization to increase the interpretability and accessibility of a collection of state-of-the-art debiasing techniques. To aid this, we present Visualization of Embedding Representations for deBiasing system (“VERB”), an open-source… 
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OSCaR: Orthogonal Subspace Correction and Rectification of Biases in Word Embeddings
TLDR
OSCaR (Orthogonal Subspace Correction and Rectification), a bias-mitigating method that focuses on disentangling biased associations between concepts instead of removing concepts wholesale, is proposed.
A Visual Tour of Bias Mitigation Techniques for Word Representations
TLDR
To help understand how various debiasing techniques change the underlying geometry, this tutorial decomposes each technique into interpretable sequences of primitive operations, and study their effect on the word vectors using dimensionality reduction and interactive visual exploration.

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