Lecture Notes in Education Psychology and Public Media

- The Open Access Proceedings Series for Conferences


Lecture Notes in Education Psychology and Public Media

Vol. 50, 26 April 2024


Open Access | Article

Research on Algorithmic Gender Bias under the Paradigm of Machine Behavior Studies

Yuqing Liu * 1
1 Hunan Normal University

* Author to whom correspondence should be addressed.

Lecture Notes in Education Psychology and Public Media, Vol. 50, 13-22
Published 26 April 2024. © 2023 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Yuqing Liu. Research on Algorithmic Gender Bias under the Paradigm of Machine Behavior Studies. LNEP (2024) Vol. 50: 13-22. DOI: 10.54254/2753-7048/50/20240802.

Abstract

In the field of communication studies, machine behavior specifically refers to information dissemination activities involving artificial intelligence technology. As algorithms increasingly become the primary force in information dissemination, their potential gender bias becomes increasingly apparent. This paper, based on three research scopes in machine behavior studies: individual behavior, collective behavior, and human-machine interaction behavior, examines the gender bias exhibited by artificial intelligence entities in algorithms at these three levels. At the individual behavior level, the tendency of algorithm development to simplify features overlooks the diversity present in female society. The inherited data bias and human bias make it difficult to avoid gender discrimination. At the collective behavior level, the creation of opinion leader-type social robots expands the subject of information fog, making the concealed “gender discrimination against women” more covert. The use of large-scale machine armies manipulates search engine results, leading to severe gender bias in search engine outputs. At the hybrid human-machine behavior level, artificial intelligence shapes female images to construct female cognitive thinking. Algorithms acquire human bias during interaction with users, and social robots amplify gender bias issues through mixed human-machine behavior.

Keywords

Machine Behavior Studies, Algorithm, Gender Bias

References

1. Rahwan, I., Cebrian, M., Obradovich, N., et al. (2019). Machine behaviour. Nature, 568(7753), 477-486.

2. Hyunjin Kang, & Chen Lou. (2022). AI Agency vs. Human Agency: Understanding Human-AI Interactions on TikTok and Their Implications for User Engagement. Journal of Computer-Mediated Communication, 27(5), 1.

3. West, M., Kraut, R., & Ei Chew, H. (2019). EQUALS y Unesco—2019—I’d Blush If I Could: Closing Gender Divides in Digital Skills through Education. UNESCO: Paris, France. 144p.

4. Meyer, D. (2018, October 10). Amazon Reportedly Killed an AI Recruitment System Because It Couldn’t Stop the Tool from Discriminating Against Women. Fortune. http://fortune.com/2018/10/10/amazon-ai-recruitment-bias-women-sexist/.

5. Zhang H., Duan Z., & Han X.. (2019). “Differentiation or Coexistence: Exploring the Research Path of Social Robots in Social Media.” Journalism, 2019(2), 10-17.

6. Wang P., Huang W., & Cao B.. (2020). “Integration and Differentiation: A Temporal Analysis of the Evolution of Diverse Subject Opinions on Weibo under the Epidemic.” Journalism University, 2020(10), 16-33, 118-119.

7. Zhang T. (2006). “Expression of Public Opinion in the Context of Media Society.” Modern Communication (Journal of Communication University of China), 2006(5), 12-15.

8. Zeng F. & Wang Y.. (2015). “The Influence of Social Media on Celebrity Public Action.” Exploration and Contention, 2015(12), 72-76.

9. Zhang H., Duan Z., & Yang H.. (2019). “Analysis of Political Robot Opinion Intervention in the Social Media Space.” Journalism, 2019(9), 17-25.

10. Heidari, M., James Jr, H., & Uzuner, O. (2021). An empirical study of machine learning algorithms for social media bot detection. 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), 1-5.

11. Yang, Q., Qureshi, K., & Zaman, T. (2021). Mitigating the backfire effect using pacing and leading. International Conference on Complex Networks and Their Applications, Springer, Cham, 156-165.

12. Zheng, C., & Fan, H. (2020). “From Social Contagion to Social Diffusion: Research on the Social Diffusion Mechanism of Social Robots.” Journalism, 2020(3), 51-62.

13. Assenmacher, D., Clever, L., Frischlich, L., et al. (2020). Demystifying social bots: On the intelligence of automated social media actors. Social Media + Society, 6(3), 2056305120939264.

14. Rahwan, I., Cebrian, M., Obradovich, N., et al. (2019). Machine behaviour. Nature, 568(7753), 477-486.

15. Chen, L. (2018). “Control and Prevention of Opinion Entropy: A Methodological Understanding of Network Governance.” Journalism and Communication Research, 2018(8), 65-80+127.

16. Zhao, B., Zhang, H., Ren, W., Zhang, Y., & Liu, S. (2022). “Tags, Accounts, and Narratives: Research on Opinion Intervention of Social Robots in the Russia-Ukraine Conflict.” Journalism and Writing, 2022(9), 89-99.

17. García-Orosa, B. (2021). Disinformation, social media, bots, and astroturfing: The fourth wave of digital democracy. Profesional de la información, 30(6).

18. Cheng, C., Luo, Y., & Yu, C. (2020). “Dynamic Mechanism of Social Bots Interfering with Public Opinion in Network.” Physica A: Statistical Mechanics and its Applications, 551, 124163.

19. Gao, X. (2019). “Research on the Influence of Intelligent Media Technology on the Evolution of Mainstream Opinion.” Modern Communication (Journal of Communication University of China), 2019(5), 5-11.

20. Zhao, B., & Zhang, H. (2022). “Issue Shifting and Attribute Highlighting: Research on the Agenda Setting of Social Robots, the Public, and the Media.” Journal of Communication and Sociology, 2022(59), 81–118.

21. Santini, R. M., Salles, D., Tucci, G., et al. (2020). Making up audience: Media bots and the falsification of the public sphere. Communication Studies, 71(3), 466-487.

22. Liu, Y. H. (2019). “Algorithmic Bias and Its Regulatory Path: A Study.” Law Journal, 2019(6), 55-66.

23. DeNardis, L. (2017). The Global War for Internet Governance (Q. Qingling & C. Huihui, Trans.). China Renmin University Press, p. 10.

24. When AI Learns Gender Discrimination. Huxiu Web. [Online]. Available: https://www.huxiu.com/article/258405.html?h_s=f1.

25. Fan, H. X., & Sun, J. B. (2021). “The Invisible ‘Elephant’: Gender Discrimination in Algorithms.” News Enthusiasts, 2021(10), 29-32. DOI:10.16017/j.cnki.xwahz.2021.10.08

26. Guo, X. P., & Qin, Y. X. (2019). “Deconstructing the Data Myth of Intelligent Communication: Causes and Risk Governance Paths of Algorithmic Bias.” Modern Communication (Journal of Communication University of China), 41(09), 19-24.

27. Morozov, E. (2014). To Save Everything, Click Here: The Folly of Technological Solutionism (X. Zhang & J. Lv, Trans.). Electronic Industry Press, pp. 156, 167, 152.

28. Hu, H. C., & Xie, X. Z.. “Development Trends and Governance Issues of Fake News under the Background of Artificial Intelligence.” News Enthusiasts.

29. Wang, H. J. (2020). “Gender Discrimination in the Consumption Scene of Artificial Intelligence.” Dialectical Naturalism Newsletter, 42(05), 45-51. DOI:10.15994/j.1000-0763.2020.05.007.

30. LoCascio, R. (2018, May 11). ‘Thousands of Sexist AI Bots Could be Coming. Here’s How We Can Stop Them.’ [Online]. Available:

31. Aurora Big Data Research Institute. (2016, December 12). Research Report on Photo Editing Apps [Research Report]. http://www.199it.com/archives/545479.html

32. Yan, J. (2019). “Algorithmic Discrimination and Countermeasures in Artificial Intelligence.” Rule of Law Review, 2019(4).

33. Wallach, W., & Allen, C. (2017). Moral Machines: How to Build Ethical Robots (X. Wang, Trans.). Peking University Press.

34. Tandoc Jr, E. C. (2019). “The Facts of Fake News: A Research Review.” Sociology Compass, 13(9), e12724.

35. Zheng, C. Y., & Fan, H. (2020). “From Social Contagion to Social Diffusion: A Study on the Social Diffusion Mechanism of Social Bots.” Journalism, 2020(3), 51-62.

36. Manjoo, F. (2017). True Enough: Learning to Live in a Post-Fact Society (J. Song, Trans.). Triple-A Publishing, p. 127.

37. Arthur, B. (2018). The Nature of Technology (D. Cao, & J. Wang, Trans.). Zhejiang People’s Publishing House, pp. 5, 36, 11, 41.

Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume Title
Proceedings of the 2nd International Conference on Social Psychology and Humanity Studies
ISBN (Print)
978-1-83558-397-5
ISBN (Online)
978-1-83558-398-2
Published Date
26 April 2024
Series
Lecture Notes in Education Psychology and Public Media
ISSN (Print)
2753-7048
ISSN (Online)
2753-7056
DOI
10.54254/2753-7048/50/20240802
Copyright
26 April 2024
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated