Skip to main content
SpringerLink
Log in

How Search Engines See European Women

  • Conference paper
  • First Online:
Electronic Participation (ePart 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14153))

Included in the following conference series:

  • 204 Accesses

Abstract

Search engine bias is a reflex of an overall social system intertwined with discriminatory, prejudiced, or biased practices of different social groups. This paper focuses on the question of ethnic-biased search engine results of European women images. The paper aims to examine whether three culturally diverse search engines (Google, Yandex, and Baidu) will result in different nudity scores for nine EU-27 selected women ethnicities.

For the paper, 100 photos of women from nine EU countries were collected using three different search engines. After that, the nudity score was calculated for the 2700 photos, and the results were compared using suitable statistical methods. The results indicate a statistically significant difference between the search engines regarding the nudity score of the collected photos, whereby we can conclude that the results of the Chinese search engine are the most liberal. At the same time, the other two are more conservative.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 44.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 59.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Crawford, K.: The atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. Yale University Press, New Haven (2021)

    Book  Google Scholar 

  2. Cetina, K.K.: Culture in global knowledge societies: knowledge cultures and epistemic cultures. Interdisc. Sci. Rev. 32, 361–375 (2007)

    Article  Google Scholar 

  3. Savoldi, B., Gaido, M., Bentivogli, L., Negri, M., Turchi, M.: Gender bias in machine translation. Trans. Assoc. Comput. Linguist. 9, 845–874 (2021)

    Article  Google Scholar 

  4. Jones, J.J., Amin, M.R., Kim, J., Skiena, S.: Stereotypical gender associations in language have decreased over time. Sociol. Sci. 7, 1–35 (2020)

    Article  Google Scholar 

  5. Draude, C., Klumbyte, G., Lücking, P., Treusch, P.: Situated algorithms: a sociotechnical systemic approach to bias. Online Inf. Rev. 44, 325–342 (2020)

    Article  Google Scholar 

  6. Vlasceanu, M., Amodio, D.M.: Propagation of societal gender inequality by internet search algorithms. Proc. Natl. Acad. Sci. 119, e2204529119 (2022)

    Article  Google Scholar 

  7. Goffman, E., Goffman, E.: Gender display. In: Gender Advertisements, pp. 1–9 (1976)

    Google Scholar 

  8. Friedman, B., Nissenbaum, H.: Bias in computer systems. ACM Trans. Inf. Syst. (TOIS) 14, 330–347 (1996)

    Article  Google Scholar 

  9. Noble, S.U.: Algorithms of oppression. In: Algorithms of Oppression, New York University Press, New York (2018)

    Google Scholar 

  10. Schwemmer, C., Knight, C., Bello-Pardo, E.D., Oklobdzija, S., Schoonvelde, M., Lockhart, J.W.: Diagnosing gender bias in image recognition systems. Socius 6, 2378023120967171 (2020)

    Article  Google Scholar 

  11. Crawford, K.: Can an algorithm be agonistic? Ten scenes from life in calculated publics. Sci. Technol. Hum. Values 41, 77–92 (2016)

    Article  MathSciNet  Google Scholar 

  12. Silva, S., Kenney, M.: Algorithms, platforms, and ethnic bias. Commun. ACM 62, 37–39 (2019)

    Article  Google Scholar 

  13. Hofstede, G.H., Hofstede, G.: Culture’s consequences: comparing values, behaviors, institutions and organizations across nations, sage (2001)

    Google Scholar 

  14. Foucault, M.: “panopticism” from” discipline & punish: The birth of the prison. Race/Ethnicity: Multidisciplinary Global Contexts 2, 1–12 (2008)

    Google Scholar 

  15. Foucault, M.: The History of Sexuality: The Use of Pleasure, vol. 2. Vintage, New York (2012)

    Google Scholar 

  16. Elias, N.: The Civilizing Process: The History of Manners: Sociogenetic and Psychogenetic Investigations, Trans. Edmund Jephcott. Basil Blackwell, Oxford (1978)

    Google Scholar 

  17. Smith, H.W.: A modest test of cross-cultural differences in sexual modesty, embarrassment and self-disclosure. Qual. Sociol. 3, 223–241 (1980)

    Article  Google Scholar 

  18. Hetsroni, A.: Sexual content on mainstream TV advertising: a cross-cultural comparison. Sex Roles 57, 201–210 (2007)

    Article  Google Scholar 

  19. Huang, Y., Lowry, D.T.: An analysis of nudity in Chinese magazine advertising: examining gender, racial and brand differences. Sex Roles 66, 440–452 (2012)

    Article  Google Scholar 

  20. Bordo, S.: Unbearable weight: femisisn, western culture, and the body (1993)

    Google Scholar 

  21. Butler, J.: Gender trouble: feminism and the subversion of identity GT. Pol. Theory 4, 4–24 (2005)

    Google Scholar 

  22. Cover, R.: The naked subject: nudity, context and sexualization in contemporary culture. Body Soc. 9, 53–72 (2003)

    Article  Google Scholar 

  23. Zito, A., Barlow, T.E., Barlow, T.E.: Body, Subject, and Power in China. University of Chicago Press, Chicago (1994)

    MATH  Google Scholar 

  24. Barta, P.I.: Gender and sexuality in Russian civilization. Routledge, London (2013)

    Book  Google Scholar 

  25. Ananthram, A.: Comparison of the best NSFW Image Moderation APIs 2018. Towards Data Science, 22 November 2018. https://towardsdatascience.com/comparison-of-the-best-nsfw-image-moderation-apis-2018-84be8da65303. Accessed 7 Jan 2023

  26. Garcia, M.B., Revano, T.F., Habal, B.G.M., Contreras, J.O., Enriquez, J.B.R.: A pornographic image and video filtering application using optimized nudity recognition and detection algorithm. In: 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM) (2018)

    Google Scholar 

  27. Moreira, D.C., Pereira, E.T., Alvarez, M.: PEDA 376K: a novel dataset for deep-learning based porn-detectors. In: 2020 International Joint Conference on Neural Networks (IJCNN) (2020)

    Google Scholar 

  28. Huang, Y., Kong, A.W.K.: Using a CNN ensemble for detecting pornographic and upskirt images. In: 2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS) (2016)

    Google Scholar 

  29. Li, K., Xing, J., Li, B., Hu, W.: Bootstrapping deep feature hierarchy for pornographic image recognition. In: 2016 IEEE International Conference on Image Processing (ICIP) (2016)

    Google Scholar 

  30. Ou, X., Ling, H., Yu, H., Li, P., Zou, F., Liu, S.: Adult image and video recognition by a deep multicontext network and fine-to-coarse strategy. ACM Trans. Intell. Syst. Technol. (TIST) 8, 1–25 (2017)

    Article  Google Scholar 

  31. Surinta, O., Khamket, T.: Recognizing pornographic images using deep convolutional neural networks. In: 2019 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT-NCON) (2019)

    Google Scholar 

  32. Zhou, K., Zhuo, L., Geng, Z., Zhang, J., Li, X.G.: Convolutional neural networks based pornographic image classification. In: 2016 IEEE Second International Conference on Multimedia Big Data (BigMM) (2016)

    Google Scholar 

  33. Kumar Thakur, R.: Detect Nudes Using Python Programming and Deep AI, Medium, 10 April 2022. https://medium.com/geekculture/detect-nudes-using-python-programming-and-deep-ai-a9be69b2e9af. Accessed 4 Jan 2023

  34. Woodie, A.: Yahoo Shares Algorithm for Identifying ‘NSFW’ Images, datanami, 3 Oct 2016. https://www.datanami.com/2016/10/03/yahoo-shares-algorithm-identifying-nsfw-images/. Accessed 7 Jan 2023

Download references

Author information

Authors and Affiliations

  1. Faculty of Tourism and Rural Development, The University of Osijek, Pozega, Croatia

    Kristian Dokic, Barbara Pisker & Gordan Paun

Authors
  1. Kristian Dokic
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Barbara Pisker
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gordan Paun
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kristian Dokic .

Editor information

Editors and Affiliations

  1. University of Continuing Education Krems, Krems an der Donau, Austria

    Noella Edelmann

  2. Ghent University, Gent, Belgium

    Lieselot Danneels

  3. University of Continuing Education Krems, Krems an der Donau, Austria

    Anna-Sophie Novak

  4. Queen Mary University of London, London, UK

    Panos Panagiotopoulos

  5. Utrecht University, Utrecht, The Netherlands

    Iryna Susha

Rights and permissions

Reprints and permissions

Copyright information

© 2023 IFIP International Federation for Information Processing

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Dokic, K., Pisker, B., Paun, G. (2023). How Search Engines See European Women. In: Edelmann, N., Danneels, L., Novak, AS., Panagiotopoulos, P., Susha, I. (eds) Electronic Participation. ePart 2023. Lecture Notes in Computer Science, vol 14153. Springer, Cham. https://doi.org/10.1007/978-3-031-41617-0_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-41617-0_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-41616-3

  • Online ISBN: 978-3-031-41617-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Societies and partnerships

Search

Navigation