SavedAdd your journal

Journals / PESA Uluslararası Sosyal Araştırmalar Dergisi / 2025 / Sayı: 2

Encoded Sexism: The Erasure of Women in Artificial Intelligence Systems

Kodlanmış Cinsiyetçilik: Yapay Zekâ Sistemlerinde Kadınların Silinmesi

Muhammed Ramazan Demirci(GİRESUN ÜNİVERSİTESİ)0000-0002-6726-7370
Pages
98–114
Publication Date
2025-12-31
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

By engaging with the implications of technological development for democratic equality and knowledge politics, the study concludes with policy recommendations for a more just and inclusive AI ecosystem.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study offers a theoretical and critical examination of the systematic erasure of women in artificial intelligence (AI) systems through the concept of “encoded sexism.” Situated at the intersection of political science and gender studies, the research is grounded in feminist epistemology and critical technology studies.
📊
Key Findings & Data:The findings demonstrate that AI systems not only reproduce existing gender inequalities but also contribute to the invisibilization of women.
💡
Conclusion & Impact:By engaging with the implications of technological development for democratic equality and knowledge politics, the study concludes with policy recommendations for a more just and inclusive AI ecosystem.
🔬Design:Araştırma Makalesi
📍Scope:Comprehensive Sample
⚙️Variables:Kodlanmış Cinsiyetçilik · Algoritmik Önyargı · Yapay Zekâ

Abstract

This study offers a theoretical and critical examination of the systematic erasure of women in artificial intelligence (AI) systems through the concept of “encoded sexism.” Situated at the intersection of political science and gender studies, the research is grounded in feminist epistemology and critical technology studies. Encoded sexism refers to the embedded gender biases that permeate all stages of AI processes—from data collection and algorithm design to user interaction. By challenging the myth of algorithmic neutrality, the study problematizes the assumption that AI is an objective and impartial tool, emphasizing instead that these systems are inherently shaped by their socio-political and ideological contexts. Utilizing feminist critical discourse analysis, the research conducts case studies in facial recognition, recruitment algorithms, and natural language processing to reveal patterns of algorithmic gender bias. The findings demonstrate that AI systems not only reproduce existing gender inequalities but also contribute to the invisibilization of women. By engaging with the implications of technological development for democratic equality and knowledge politics, the study concludes with policy recommendations for a more just and inclusive AI ecosystem.

Özet

Bu çalışma, yapay zekâ sistemlerinde kadınların sistematik silinişini “kodlanmış cinsiyetçilik” kavramı çerçevesinde kuramsal ve eleştirel olarak incelemektedir. Siyaset bilimi ve kadın çalışmaları kesişiminde konumlanan araştırma, feminist epistemoloji ve teknoloji eleştirisine dayanan bir kuramsal temel üzerine inşa edilmiştir. Kodlanmış cinsiyetçilik, veri setlerinden algoritma tasarımına ve kullanıcı deneyimine dek yapay zekâ süreçlerine sızan toplumsal cinsiyet önyargılarının bütüncül bir ifadesidir. Çalışmada, algoritmik tarafsızlık mitinin eleştirisi yapılarak yapay zekânın tarafsız ve nesnel bir araç olduğu iddiası sorgulanmakta; bunun yerine bu sistemlerin toplumsal, politik ve ideolojik bağlamlardan bağımsız düşünülemeyeceği vurgulanmaktadır. Feminist eleştirel söylem analizi yöntemiyle gerçekleştirilen vaka incelemeleri, yüz tanıma, işe alım ve dil işleme alanlarındaki algoritmik cinsiyetçilik örüntülerini ortaya koymaktadır. Elde edilen bulgular, yapay zekâ sistemlerinin mevcut toplumsal cinsiyet eşitsizliklerini yeniden ürettiğini ve kadınları görünmez kılabildiğini göstermektedir. Çalışma, teknolojik gelişmelerin demokratik eşitlik ve bilgi politikaları açısından sonuçlarını tartışarak, adalet ve kapsayıcılığı gözeten bir yapay zekâ ekosistemi için politika önerileri sunmaktadır.

🔖

Cite This Article

Demirci, M. R. (2025). Encoded Sexism: The Erasure of Women in Artificial Intelligence Systems. PESA Uluslararası Sosyal Araştırmalar Dergisi, Sayı: 2, 98–114. https://ajindex.com/articles/1536097

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (39)

  1. Ajunwa, I. (2020). The paradox of automation as anti-bias intervention. Cardozo Law Review, 41(4), 1671–1742. http://dx.doi.org/10.2139/ssrn.2746078
  2. Barocas, S., & Selbst, A. D. (2016). Big data’s disparate impact. California Law Review, 104, 671–732. https://doi.org/10.2139/ssrn.2477899
  3. Benjamin, R. (2019). Race after technology: Abolitionist tools for the New Jim Code. Cambridge: Polity Press.
  4. Bolukbasi, T., Chang, K.-W., Zou, J., Saligrama, V., & Kalai, A. (2016). Man is to computer programmer as woman is to homemaker? Debiasing word embeddings. In 30th Conference on Neural Information Processing Systems (NIPS 2016) (pp. 1–9). Barcelona, Spain. https://doi.org/10.48550/arXiv.1607.06520
  5. Boyd, D., & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication & Society, 15(5), 662–679. https://doi.org/10.1080/1369118X.2012.678878
  6. Buolamwini, J., & Gebru, T. (2018). Gender shades: Intersectional accuracy disparities in commercial gender classification. In Proceedings of the 1st Conference on Fairness, Accountability and Transparency (pp. 77–91). PMLR 81. https://proceedings.mlr.press/v81/buolamwini18a.html
  7. Caliskan, A., Bryson, J. J., & Narayanan, A. (2017). Semantics derived automatically from language corpora contain human-like biases. Science, 356(6334), 183–186. https://doi.org/10.1126/science.aal4230
  8. Cockburn, C., & Ormrod, S. (1993). Gender and technology in the making. London: SAGE Publications.
  9. Cockburn, C. (1983). Brothers: Male dominance and technological change. London: Pluto Press.
  10. Code, L. (1991). What can she know? Feminist theory and the construction of knowledge. Ithaca: Cornell University Press.
  11. Costanza-Chock, S. (2020). Design justice: Community-led practices to build the worlds we need. Cambridge: MIT Press.
  12. Crawford, K., & Paglen, T. (2019). Excavating AI: The politics of images in machine learning training sets. The AI Now Institute. https://www.excavating.ai/
  13. Crawford, K. (2021). Atlas of AI: Power, politics, and the planetary costs of artificial intelligence. New Haven: Yale University Press.
  14. Criado Perez, C. (2019). Invisible women: Exposing data bias in a world designed for men. New York: Abrams Press.
  15. D’Ignazio, C., & Klein, L. F. (2020). Data feminism. Cambridge: MIT Press.
  16. Daily Sabah. (2018, December 7). Google moves to fix gender bias in its Translate service. Daily Sabah. https://www.dailysabah.com/technology/2018/12/07/google-moves-to-fix-gender-bias-in-its-translate-service (accessed May 21, 2025).
  17. Dastin, J. (2018, October 10). Insight - Amazon scraps secret AI recruiting tool that showed bias against women. Reuters. https://www.reuters.com/article/world/insight-amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK0AG/ (accessed May 17, 2025).
  18. Dotson, K. (2011). Tracking epistemic violence, tracking practices of silencing. Hypatia, 26(2), 236–257. https://doi.org/10.1111/j.1527-2001.2011.01177.x
  19. Eubanks, V. (2017). Automating inequality: How high-tech tools profile, police, and punish the poor. New York: St. Martin’s Press.
  20. Faulkner, W. (2001). The technology question in feminism: A view from feminist technology studies. Women’s Studies International Forum, 24(1), 79–95. https://doi.org/10.1016/S0277-5395(00)00166-7
  21. Faulkner, W. (2007). Nuts and bolts and people: Gender-troubled engineering identities. Social Studies of Science, 37(3), 331–356. https://doi.org/10.1177/0306312706072175
  22. Fricker, M. (2007). Epistemic injustice: Power and the ethics of knowing. Oxford: Oxford University Press.
  23. Haraway, D. (1988). Situated knowledges: The science question in feminism and the privilege of partial perspective. Feminist Studies, 14(3), 575–599. https://doi.org/10.2307/3178066
  24. Harding, S. (1991). Whose science? Whose knowledge? Thinking from women’s lives. Ithaca: Cornell University Press.
  25. Keyes, O. (2018). The misgendering machines: Trans/HCI implications of automatic gender recognition. Proceedings of the ACM on Human-Computer Interaction, 2(CSCW), 1–22. https://doi.org/10.1145/3274357
  26. Kuczmarski, J. (2018, December 6). Reducing gender bias in Google Translate. Google Blog. https://blog.google/products/translate/reducing-gender-bias-google-translate/ (accessed June 2, 2025).
  27. Lambrecht, A., & Tucker, C. (2019). Algorithmic bias? An empirical study of apparent gender-based discrimination in the display of STEM career ads. Management Science, 65(7), 2947–3448. https://doi.org/10.1287/mnsc.2018.3093
  28. Lazar, M. M. (2007). Feminist critical discourse analysis: Articulating a feminist discourse praxis. Critical Discourse Studies, 4(2), 141–164. https://doi.org/10.1080/17405900701464816
  29. Medina, J. (2013). The epistemology of resistance: Gender and racial oppression, epistemic injustice, and resistant imaginations. Oxford: Oxford University Press.
  30. Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism. New York: New York University Press.
  31. Prates, M. O. R., Avelar, P. H., & Lamb, L. C. (2020). Assessing gender bias in machine translation: A case study with Google Translate. Neural Computing and Applications, 32, 6363–6381. https://doi.org/10.1007/s00521-019-04144-6
  32. Raghavan, M., Barocas, S., Kleinberg, J., & Levy, K. (2020). Mitigating bias in algorithmic hiring: Evaluating claims and practices. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (pp. 469–481). https://doi.org/10.1145/3351095.3372828
  33. Raji, I. D., Gebru, T., Mitchell, M., Buolamwini, J., Lee, J., & Denton, R. (2020). Saving face: Investigating the ethical concerns of facial recognition auditing. In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (pp. 145–151). https://doi.org/10.1145/3375627.3375820
  34. Seyfert, R., & Roberge, J. (2016). Algorithmic cultures: Essays on meaning, performance and new technologies. London: Routledge.
  35. Striphas, T. (2015). Algorithmic culture. European Journal of Cultural Studies, 18(4–5), 395–412. https://doi.org/10.1177/1367549415577392
  36. Sunderland, J. (2004). Gendered discourses. Basingstoke: Palgrave Macmillan.
  37. Veale, M., & Edwards, L. (2018). Clarity, surprises, and further questions in the Article 29 Working Party draft guidance on automated decision-making and profiling. Computer Law & Security Review, 34(2), 398–404. https://doi.org/10.1016/j.clsr.2017.12.002
  38. Wajcman, J. (2004). TechnoFeminism. Cambridge: Polity Press.
  39. Weber, M. (1978). Economy and society: An outline of interpretive sociology (G. Roth & C. Wittich, Eds.). Berkeley: University of California Press.