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Did You Just Assume My Vector? Detecting Gender Stereotypes in Word Embeddings

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Recent Trends in Analysis of Images, Social Networks and Texts (AIST 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1357))

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Abstract

Recent studies found out that supervised machine learning models can capture prejudices and stereotypes from training data. Our study focuses on the detection of gender stereotypes in relation to word embeddings. We review prior work on the topic and propose a comparative study of existing methods of gender stereotype detection. We evaluate various word embeddings models with these methods and conclude that the amount of bias does not depend on the corpora size and training algorithm, and does not correlate with embeddings performance on the standard evaluation benchmarks.

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Notes

  1. 1.

    Here and after we will use L2-normalized Euclidian distance in all cases where we measure the distance between the vectors: assuming that the vectors are normalized, the choice between Euclidian distance and cosine similarity does not affect their results [17].

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Acknowledgments

The reported study was funded by the Russian Foundation for Basic Research project 20-37-90153 “Development of framework for distributional semantic models evaluation”.

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Authors and Affiliations

  1. National Research University Higher School of Economics, Moscow, Russia

    Amir Bakarov

Authors
  1. Amir Bakarov
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Correspondence to Amir Bakarov .

Editor information

Editors and Affiliations

  1. RWTH Aachen University, Aachen, Germany

    Wil M. P. van der Aalst

  2. University of Ljubljana, Ljubljana, Slovenia

    Vladimir Batagelj

  3. National Research University Higher School of Economics, Perm, Russia

    Alexey Buzmakov

  4. National Research University Higher School of Economics, Moscow, Russia

    Dmitry I. Ignatov

  5. University of Melbourne, Melbourne, VIC, Australia

    Anna Kalenkova

  6. Krasovskii Institute of Mathematics and Mechanics of RAS, Ekaterinburg, Russia

    Michael Khachay

  7. National Research University Higher School of Economics, Saint-Petersburg, Russia

    Olessia Koltsova

  8. University of Oslo, Oslo, Norway

    Andrey Kutuzov

  9. National Research University Higher School of Economics, Moscow, Russia

    Sergei O. Kuznetsov

  10. National Research University Higher School of Economics, Moscow, Russia

    Irina A. Lomazova

  11. Lomonosov Moscow State University, Moscow, Russia

    Natalia Loukachevitch

  12. National Research University Higher School of Economics, Moscow, Russia

    Ilya Makarov

  13. LORIA, Vandœuvre-lès-Nancy, France

    Amedeo Napoli

  14. Skolkovo Institute of Science and Technology, Moscow, Russia

    Alexander Panchenko

  15. University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

  16. Università Ca’ Foscari Venezia, Venezia, Italy

    Marcello Pelillo

  17. National Research University Higher School of Economics, Nizhny Novgorod, Russia

    Andrey V. Savchenko

  18. Kazan Federal University, Kazan, Russia

    Elena Tutubalina

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Bakarov, A. (2021). Did You Just Assume My Vector? Detecting Gender Stereotypes in Word Embeddings. In: van der Aalst, W.M.P., et al. Recent Trends in Analysis of Images, Social Networks and Texts. AIST 2020. Communications in Computer and Information Science, vol 1357. Springer, Cham. https://doi.org/10.1007/978-3-030-71214-3_1

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  • DOI: https://doi.org/10.1007/978-3-030-71214-3_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-71213-6

  • Online ISBN: 978-3-030-71214-3

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