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Biased Embeddings from Wild Data: Measuring, Understanding and Removing

  • Adam Sutton
  • Thomas Lansdall-WelfareEmail author
  • Nello Cristianini
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11191)

Abstract

Many modern Artificial Intelligence (AI) systems make use of data embeddings, particularly in the domain of Natural Language Processing (NLP). These embeddings are learnt from data that has been gathered “from the wild” and have been found to contain unwanted biases. In this paper we make three contributions towards measuring, understanding and removing this problem. We present a rigorous way to measure some of these biases, based on the use of word lists created for social psychology applications; we observe how gender bias in occupations reflects actual gender bias in the same occupations in the real world; and finally we demonstrate how a simple projection can significantly reduce the effects of embedding bias. All this is part of an ongoing effort to understand how trust can be built into AI systems.

Keywords

Fairness in AI Bias in data Artificial intelligence Natural language processing Word embeddings 

Notes

Acknowledgements

AS is supported by EPSRC Centre for Communications. TLW and NC are support by the FP7 Ideas: European Research Council Grant 339365 - ThinkBIG.

References

  1. 1.
    Angwin, J., Larson, J., Mattu, S., Kirchner, L.: Machine bias: theres software used across the country to predict future criminals. and its biased against blacks. ProPublica, May 23 2016 (2016)Google Scholar
  2. 2.
    Bolukbasi, T., Chang, K.W., Zou, J.Y., Saligrama, V., Kalai, A.T.: Man is to computer programmer as woman is to homemaker? Debiasing word embeddings. In: Advances in Neural Information Processing Systems, pp. 4349–4357 (2016)Google Scholar
  3. 3.
    Caliskan, A., Bryson, J.J., Narayanan, A.: Semantics derived automatically from language corpora contain human-like biases. Science 356(6334), 183–186 (2017)CrossRefGoogle Scholar
  4. 4.
    Cer, D., Diab, M., Agirre, E., Lopez-Gazpio, I., Specia, L.: Semeval-2017 task 1: semantic textual similarity-multilingual and cross-lingual focused evaluation. arXiv preprint arXiv:1708.00055 (2017)
  5. 5.
    Flaounas, I., Ali, O., Lansdall-Welfare, T., De Bie, T., Mosdell, N., Lewis, J., Cristianini, N.: Research methods in the age of digital journalism: massive-scale automated analysis of news-contenttopics, style and gender. Dig. Journal. 1(1), 102–116 (2013)Google Scholar
  6. 6.
    Flores, A.W., Bechtel, K., Lowenkamp, C.T.: False positives, false negatives, and false analyses: a rejoinder to machine bias: there’s software used across the country to predict future criminals and it’s biased against blacks. Fed. Probat. 80, 38 (2016)Google Scholar
  7. 7.
    Fong, R., Vedaldi, A.: Net2Vec: quantifying and explaining how concepts are encoded by filters in deep neural networks. arXiv preprint arXiv:1801.03454 (2018)
  8. 8.
    Greenwald, A.G., McGhee, D.E., Schwartz, J.L.: Measuring individual differences in implicit cognition: the implicit association test. J. Personal. Soc. Psychol. 74(6), 1464 (1998)CrossRefGoogle Scholar
  9. 9.
    Jia, S., Lansdall-Welfare, T., Cristianini, N.: Freudian slips: analysing the internal representations of a neural network from its mistakes. In: Advances in Intelligent Data Analysis XVI, pp. 138–148 (2017)CrossRefGoogle Scholar
  10. 10.
    Jia, S., Lansdall-Welfare, T., Sudhahar, S., Carter, C., Cristianini, N.: Women are seen more than heard in online newspapers. PLOS ONE 11(2), 1–11 (2016).  https://doi.org/10.1371/journal.pone.0148434CrossRefGoogle Scholar
  11. 11.
    Kahng, M., Andrews, P.Y., Kalro, A., Chau, D.H.P.: Activis: visual exploration of industry-scale deep neural network models. IEEE Trans. Vis. Comput. Gr. 24(1), 88–97 (2018)CrossRefGoogle Scholar
  12. 12.
    Lansdall-Welfare, T., Sudhahar, S., Thompson, J., Lewis, J., Team, F.N., Cristianini, N., Gregor, A., Low, B., Atkin-Wright, T., Dobson, M.: Content analysis of 150 years of british periodicals. Proc. Natl. Acad. Sci. 114(4), E457–E465 (2017)CrossRefGoogle Scholar
  13. 13.
    Miller, G.A.: Wordnet: a lexical database for english. Commun. ACM 38(11), 39–41 (1995)CrossRefGoogle Scholar
  14. 14.
    Nakov, P., Ritter, A., Rosenthal, S., Sebastiani, F., Stoyanov, V.: SemEval-2016 task 4: sentiment analysis in twitter. In: Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), pp. 1–18 (2016)Google Scholar
  15. 15.
    Office for National Statistics: Statistical bulletin: Annual survey of hours and earnings: 2017 provisional and 2016 revised results (2017). https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/earningsandworkinghours/bulletins/annualsurveyofhoursandearnings/2017provisionaland2016revisedresults
  16. 16.
    Parker, R., Graff, D., Kong, J., Chen, K., Maeda, K.: English Gigaword Fifth Edition ldc2011t07. DVD. Linguistic Data Consortium, Philadelphia (2011)Google Scholar
  17. 17.
    Pennebaker, J.W., Francis, M.E., Booth, R.J.: Linguistic Inquiry and Word Count: LIWC 2007. Mahway: Lawrence Erlbaum Associates, vol. 71 (2001)Google Scholar
  18. 18.
    Pennington, J., Socher, R., Manning, C.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014)Google Scholar
  19. 19.
    Ribeiro, M.T., Singh, S., Guestrin, C.: Why should i trust you? Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135–1144 (2016)Google Scholar
  20. 20.
    Samek, W., Binder, A., Montavon, G., Lapuschkin, S., Müller, K.R.: Evaluating the visualization of what a deep neural network has learned. IEEE Trans. Neural Netw. Learn. Syst. (2017)Google Scholar
  21. 21.
    Shrivastava, A., Pfister, T., Tuzel, O., Susskind, J., Wang, W., Webb, R.: Learning from simulated and unsupervised images through adversarial training. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 3, p. 6 (2017)Google Scholar
  22. 22.
    Silver, D., Huang, A., Maddison, C.J., Guez, A., Sifre, L., Van Den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al.: Mastering the game of go with deep neural networks and tree search. Nature 529(7587), 484–489 (2016)CrossRefGoogle Scholar
  23. 23.
    Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. arXiv preprint arXiv:1703.10593 (2017)

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Adam Sutton
    • 1
  • Thomas Lansdall-Welfare
    • 1
    Email author
  • Nello Cristianini
    • 1
  1. 1.Intelligent Systems LaboratoryUniversity of BristolBristolUK

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