A Visual Tour of Bias Mitigation Techniques for Word Representations

Part of KDD 2021 Tutorial.

A virtual tutorial, schedule TBA.

Visual Demo Download: https://github.com/tdavislab/visualizing-bias

Organizers


Archit Rathore
Ph.D. Student, School of Computing
Scientific Computing and Imaging (SC) Institute
University of Utah
architrathore1 AT gmail.com

Archit Rathore is a Ph.D. student at the School of Computing at the University of Utah. His current research focuses on probing machine learning models through visualization techniques to improve interpretability.

Sunipa Dev
Postdoctoral researcher
University of California, Los Angeles (UCLA)
sunipa AT cs.ucla.edu

Sunipa Dev received her Ph.D. at the School of Computing at the University of Utah in Fall 2020 and is a CI Postdoctoral Fellow at UCLA. Her research focuses on understanding the structure of language representations and leveraging that to isolate and decouple associations and concept subspaces within.

Jeff Phillips
Associate Professor, School of Computing
University of Utah
jeffp AT cs.utah.edu

Jeff M. Phillips is an Associate Professor at the School of Computing, and Director of the Utah Center for Data Science, at the University of Utah. He is an expert in the geometry of data, and actively publishes in top venues in machine learning & data mining, algorithms & geometry, and databases.

Vivek Srikumar
Associate Professor, School of Computing
University of Utah
svivek AT cs.utah.edu

Vivek Srikumar is an Associate Professor at the School of Computing at the University of Utah. His research focuses on machine learning in the context of natural learning processing and has primarily been driven by questions arising from the need to reason about textual data with limited explicit supervision and to scale NLP to large problems.

Bei Wang
Assistant Professor, School of Computing
Scientific Computing and Imaging (SC) Institute
University of Utah
beiwang AT sci.utah.edu

Bei Wang is an Assistant Professor at the School of Computing, a faculty member in the Scientific Computing and Imaging (SCI) Institute, University of Utah. Her research interests include data visualization, topological data analysis, computational topology, computational geometry, machine learning, and data mining.

Overview

Motivation

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 tutorial, we will review a collection of state-of-the-art debiasing techniques. To aid this, we provide an open source web-based visualization tool and offer hands-on experience in exploring the effects of these debiasing techniques on the geometry of high-dimensional word vectors. To help understand how various debiasing techniques change the underlying geometry, we decompose each technique into interpretable sequences of primitive operations and study their effect on the word vectors using dimensionality reduction and interactive visual exploration.

Prerequisite Knowledge

The attendees are expected to understand the basics of linear algebra and dimensionality reduction. Familiarity with basic NLP would be helpful but is not required. The audience is not assumed to have knowledge about bias in NLP; however, the tutorial will still be applicable to those who do.

Tutorial Materials

Coming soon.

References

Please email Bei Wang (beiwang AT sci.utah.edu) if a certain paper should be added to the list.

  1. OSCaR: Orthogonal Subspace Correction and Rectification of Biases in Word Embeddings.
    Sunipa Dev and Tao Li and Jeff M Phillips and Vivek Srikumari
    arXiv preprint arXiv:2007.00049  (2020)

  2. What are the biases in my word embedding?
    N. Swinger and M. De-Arteaga and N. T. H. IV and M. D. M. Leiserson and A. T. Kalai
    arxiv preprint arXiv:1812.08769      (2018)

  3. The Trouble with Bias
    K. Crawford
    Conference on Neural Information Processing Systems, Keynote      (2017)

  4. Social bias in Elicited Natural Language Inferences
    R. Rudinger and C. May and B. Van Durme
    Proceedings of the 1st ACL Workshop on Ethics in Natural Language Processing    74-79  (2017)

  5. On Measuring Social Biases in Sentence Encoders
    C. May and A. Wang and S. Bordia and S. R. Bowman and R. Rudinger
    arxiv preprint arXiv:1903.10561      (2019)
    http://arxiv.org/abs/1903.10561

  6. On Measuring and Mitigating Biased Inferences of Word Embeddings
    S. Dev and T. Li and J. M. Phillips and V. Srikumar
    AAAI      (2020)

  7. Offline bilingual word vectors, orthogonal transformations and the inverted softmax
    S. L. Smith and D. H. P. Turban and S. Hamblin and N. Y. Hammerla
    International Conference on Learning Representations      (2017)

  8. Null It Out: Guarding Protected Attributes by Iterative Nullspace Projection
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    Proceedings of the 1st ACL Workshop on Ethics in Natural Language Processing      (2017)
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    Proceedings of the 2019 SIAM International Conference on Data Mining      (2019)
    https://epubs.siam.org/doi/pdf/10.1137/1.9781611975673.90
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