Journals / KADEM Kadın Araştırmaları Dergisi / 2026 / Sayı: 1 Özel Sayı

What is said? What is silenced? Representations of Motherhood and Discursive Invisibility in Artificial Intelligence: The Cases of Gaza and East Turkestan

Neyi Söylüyor, Neyi Susturuyor? Gazze ve Doğu Türkistan Bağlamında Yapay Zekâda Annelik Temsilleri ve Söylemsel Görünmezlik

Alperen Avcı(MUŞ ALPARSLAN ÜNİVERSİTESİ)0000-0003-2395-9495
Esra Demir Öztürk(MUŞ ALPARSLAN ÜNİVERSİTESİ)0000-0003-4996-0355
Pages
127–159
Publication Date
2026-09-30
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AI Summary & Key Takeaways

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

The findings reveal that both models produce representations that humanise motherhood; however, these representations vary significantly depending on the context.

📌 3 Key Takeaways

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Objective & Hypothesis:This study aims to examine how generative artificial intelligence systems shape gender representations and which experiences they render discursively visible or invisible.
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Key Findings & Data:In this context, the responses generated by ChatGPT, a U.S.-based large language model, and DeepSeek, a China-based model, regarding motherhood, femininity, and caregiving experiences in the contexts of Gaza and East Turkestan were comparatively analysed.
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Conclusion & Impact:The findings reveal that both models produce representations that humanise motherhood; however, these representations vary significantly depending on the context.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Yapay zekâ · Toplumsal cinsiyet · Annelik temsilleri

Abstract

This study aims to examine how generative artificial intelligence systems shape gender representations and which experiences they render discursively visible or invisible. In this context, the responses generated by ChatGPT, a U.S.-based large language model, and DeepSeek, a China-based model, regarding motherhood, femininity, and caregiving experiences in the contexts of Gaza and East Turkestan were comparatively analysed. The study was conducted using a qualitative content analysis and comparative discourse analysis approach. Data obtained from a total of 60 prompts were evaluated in terms of mechanisms of discursive visibility, omission, and reframing. The findings reveal that both models produce representations that humanise motherhood; however, these representations vary significantly depending on the context. ChatGPT, particularly in the context of Gaza, tends to make human suffering visible while avoiding explicit identification of the perpetrators of violence and political responsibility, thereby producing a neutralising discourse. In contrast, DeepSeek, in the context of East Turkestan, tends to avoid representation, suppress certain narratives, and at times generate a discourse that may be interpreted as implicitly legitimising. These findings suggest that artificial intelligence systems are not merely tools for information generation but also epistemic actors that selectively construct gender representations. The study contributes to the literature on AI ethics and feminist technology studies by demonstrating that bias in artificial intelligence should be evaluated not only through explicit prejudice but also through processes of discursive invisibility and omission.

Özet

Bu çalışma; üretken yapay zekâ sistemlerinin toplumsal cinsiyet temsillerini nasıl şekillendirdiğini, hangi deneyimleri söylemsel düzeyde görünür kılarken hangilerini görünmez kıldığını incelemeyi amaçlamaktadır. Bu doğrultuda; Amerika Birleşik Devletleri merkezli bir büyük dil modeli olan ChatGPT ile Çin merkezli DeepSeek’in, Gazze ve Doğu Türkistan bağlamlarında annelik, kadınlık ve bakım deneyimlerine ilişkin ürettikleri yanıtlar karşılaştırmalı olarak analiz edilmiştir. Araştırma, nitel içerik analizi ve karşılaştırmalı söylem analizi yaklaşımlarıyla yürütülmüş; toplam 60 istem (prompt) üzerinden elde edilen veriler; söylemsel görünürlük, eksiltme ve yeniden çerçeveleme mekanizmaları açısından değerlendirilmiştir. Bulgular, her iki modelin de anneliği insanileştiren temsiller ürettiğini ancak bu temsillerin bağlama göre önemli farklılıklar gösterdiğini ortaya koymaktadır. ChatGPT’nin özellikle Gazze bağlamında insani acıyı görünür kılarken şiddetin failini ve politik sorumluluğu açık biçimde adlandırmaktan kaçınarak nötrleştirici bir söylem ürettiği; DeepSeek’in ise Doğu Türkistan bağlamında temsil üretmekten kaçınan, bastıran ve yer yer meşrulaştırıcı bir söylem geliştirdiği saptanmıştır. Bu bulgular; yapay zekâ sistemlerinin yalnızca bilgi üreten araçlar değil, aynı zamanda toplumsal cinsiyet temsillerini seçici biçimde yapılandıran epistemik aktörler olduğunu göstermektedir. Çalışma; yapay zekâda yanlılığın yalnızca açık önyargılar üzerinden değil, söylemsel görünmezlik ve eksiltme süreçleri üzerinden de değerlendirilmesi gerektiğini ortaya koyarak yapay zekâ etiği ve feminist teknoloji çalışmaları literatürüne katkı sunmaktadır.

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Cite This Article

Avcı, A., Öztürk, E. D. (2026). What is said? What is silenced? Representations of Motherhood and Discursive Invisibility in Artificial Intelligence: The Cases of Gaza and East Turkestan. KADEM Kadın Araştırmaları Dergisi, Sayı: 1 Özel Sayı, 127–159. https://doi.org/10.21798/kadem.2026.220

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References (43)

  1. Abid, A., Farooqi, M., & Zou, J. (2021). Persistent anti-Muslim bias in large language models. In Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society (pp. 298–306). https://doi.org/10.1145/3461702.3462624
  2. Abu Watfa, Y. (2016, May 8). Gaza women face siege, poverty, and unemployment. The New Arab.
  3. Aktan, S. (2020, Agust 3). Rapor: Zorla doğum kontrolü ve kısırlaştırma ile Uygur nüfusu azaltılıyor. Euronews. Retrieved from https://tr.euronews.com/2020/08/03/rapor-zorla-dogum-kontrolu-ve-k-s-rlast-rma-ileuygur- nufusu-azalt-l-yor
  4. Al Jazeera. (2017, March ناودعلاو راصحلا نمث نعفدي ةزغ ءاسن.( 8 (Gaza women pay the price of siege and aggression). Retrieved from https://www.aljazeera.net/news/2017/3/ ناودعلاو-راصحلا-نمث-نعفدي-ةزغ-ءاسن/ 8
  5. Amnesty International. (1996). China: No one is safe (ASA 17/002/1996).
  6. Amnesty International. (2021, April 7). Amnesty International Report 2020/21: The state of the world’s human rights. Retrieved April 10, 2021, from https://www.amnesty.org/download/Documents/POL1032022021TURKISH. PDF
  7. Angwin, J., Larson, J., Mattu, S., & Kirchner, L. (2022). Machine bias. In K. Martin, Ethics of data and analytics: Concepts and cases (1st ed., pp. 11–18). Auerbach Publications.
  8. Ashour, S. A., Migdad, M., & Buheji, M. (2025). The impact of war on Gaza on children’s malnutrition since 2023. Public Health Research, 15(1), 1–9.
  9. Bag, M. (2019, July 7). RFA: Doğu Türkistanlı kadınlar, Çinli erkek görevlilerle yatağını paylaşmaya zorlanıyor. Euronews. Retrieved from https://tr.euronews.com/2019/07/10/22-ulkeden-cine-dogu-turkistan-tepkisikitlesel-gozaltilar-derhal-durdur-uygurlar-serbest
  10. Banat, B. (2018). Palestinian women and resistance. International Journal of ANGLISTICUM, 7(3), 44–55.
  11. Cirillo, D., Catuara-Solarz, S., Morey, C., Guney, E., Subirats, L., Mellino, S., Gigante,
  12. A., Valencia, A., Rementeria, M. J., Chadha, A. S., & Mavridis, N. (2020). Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare. npj Digital Medicine, 3(1), 1–11. https://doi.org/10.1038/ s41746-020-0288-5
  13. Crawford, K., & Paglen, T. (2021). Excavating AI: The politics of images in machine learning training sets. AI & Society, 36(4), 1105–1116. https://doi.org/10.1007/s00146-021-01162-8
  14. de Waal, A. (2025). Starvation is a lifelong sentence: Gaza’s civilians must be protected in accordance with international humanitarian law. BMJ, 390, r1607.
  15. Ferrara, E. (2023). Should ChatGPT be biased? Challenges and risks of bias in large language models. arXiv. https://doi.org/10.48550/arXiv.2304.03738
  16. Ferrara, E. (2024). Fairness and bias in artificial intelligence: A brief survey of sources, impacts and mitigation strategies. Sci, 6(1), Article 1. https://doi.org/10.3390/sci6010003
  17. Frąckiewicz, M. (2023, May 17). ChatGPT and the risks of deepening political polarization and divides. TS2 Space Blog. Retrieved from https://ts2.space/en/chatgpt-and-the-risks-of-deepening-political-polarizationand- divides
  18. Hamamra, B., Shehab, E., & Mayaleh, A. (2025). The moral weight of watching children starve: Maternal voices on systematic deprivation in Gaza. Journal of Asian and African Studies. Advance online publication. https://doi.org/10.1177/00219096251389870
  19. Ho, J. Q., Hartanto, A., Koh, A., & Majeed, N. M. (2025). Gender biases within artificial intelligence and ChatGPT: Evidence, sources of biases and solutions. Computers in Human Behavior: Artificial Humans, 100145. https://doi.org/10.1016/j.chbah.2025.100145
  20. Human Rights Watch. (2021, April 19). “Break their lineage, break their roots”: Chinese government crimes against humanity targeting Uyghurs and other Turkic Muslims. Retrieved from https://www.hrw.org
  21. Kaplan, D. M., Palitsky, R., Arconada Alvarez, S. J., Pozzo, N. S., Greenleaf, M. N., Atkinson, C. A., & Lam, W. A. (2024). What’s in a name? Experimental evidence of gender bias in recommendation letters generated by ChatGPT. Journal of Medical Internet Research, 26, e51837. https://doi.org/10.2196/51837
  22. Kovner, B., & Shalhoub-Kevorkian, N. (2018). Children, human rights organisations, and the law under occupation: The case of Palestinian children in East Jerusalem. The International Journal of Human Rights, 22(5), 616–639. https://doi.org/10.1080/13642987.2017.1397635
  23. McKenna, M. (2019, October 5). Three notable examples of AI bias. Retrieved from https://aibusiness.com/responsible-ai/three-notable-examples-ofai-bias
  24. Miles, M. B., & Huberman, A. M. (1994). Qualitative data analysis: An expanded sourcebook (2nd ed.). Sage.
  25. Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism. New York University Press.
  26. O’Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown Publishing Group.
  27. Parsheera, S. (2018). A gendered perspective on artificial intelligence. In Machine learning for a 5G future (pp. 1–7). https://doi.org/10.23919/ITUWT.2018.8597618
  28. Paryente, B. (2025). Intergenerational motherhood in the Gaza envelope: Coping, resilience, and community boundaries in a conflict zone. Journal of Child and Family Studies, 34, 3149–3160. https://doi.org/10.1007/s10826-025-03208-7
  29. Ray, P. P. (2023). ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope. Internet of Things and Cyber-Physical Systems, 3, 121–154. https://doi.org/10.1016/j. iotcps.2023.04.003
  30. Ruser, N. (2020). Documenting Xinjiang’s detention system. Australian Strategic Policy Institute. Retrieved from https://cdn.xjdp.aspi.org.au/wp-content/uploads/2020/09/25125443/documenting-xinjiangs-detention-system.cleaned.pdf
  31. Ryan, F., Ceva, D. & Ruser, N. (2018). Mapping Xinjiang’s ‘re-education’ camps. Australian Strategic Policy Institute. Retrieved from https://www.aspi.org.au/report/mapping-xinjiangs-re-education-camps
  32. Sandoval Martin, T., & Martinez Sanzo, E. (2024). Perpetuation of gender bias in visual representation of professions in generative AI tools. Social Sciences, 13(5), 250. https://doi.org/10.3390/socsci13050250
  33. Shrestha, S., & Das, S. (2022). Exploring gender biases in ML and AI academic research through systematic literature review. Frontiers in Artificial Intelligence, 5, 976838. https://doi.org/10.3389/frai.2022.976838
  34. Sourani, G. (2011). The blockade and division and their economic and social effects on the Gaza Strip.
  35. Türköz, A. (1998). Doğu Türkistan`da insan hakları/Human Rights in East Turkestan. (Ph.D. Dissertation). İstanbul University.
  36. UNICEF. (2024a). Children’s lives threatened by rising malnutrition in the Gaza Strip. Retrieved from https://www.unicef.org
  37. UNICEF. (2024b). State of Palestine appeal: Humanitarian action for children 2024. Retrieved from https://www.unicef.org
  38. UNICEF. (2024c). State of Palestine humanitarian situation report: Mid-year 2024. Retrieved from https://www.unicef.org
  39. West, S. M., Whittaker, M., & Crawford, K. (2019). Discriminating systems: Gender, race, and power in AI. AI Now Institute.
  40. Xu, V. X., Cave, D., Leibold, J., Munro, K., & Ruser, N. (2020). Uyghurs for sale. Re-education’, forced labour and surveillance beyond Xinjiang. Australian Strategic Policy Institute. Retrieved from https://respect.international/ wp-content/uploads/2020/06/Uyghurs-for-sale-‘Re-education’-forcedlabour-and-surveillance-beyond-Xinjiang-.pdf
  41. Zenz, A. (2019). Thoroughly reforming them towards a healthy heart attitude: China’s political re-education campaign in Xinjiang. Central Asian Survey, 38(1), 102–128. https://doi.org/10.1080/02634937.2018.1507997
  42. Zenz, A. (2020). A Response to the Report Compiled by Lin Fangfei, Associate Professor at Xinjiang University. Retrieved from https://adrianzenz.medium.com/a-response-to-the-report-compiled-by-lin-fangfeiassociate-professor-at-xinjiang-university-bdad4bbb97f9
  43. Al Jazeera. (2017, March ناودعلاو راصحلا نمث نعفدي ةزغ ءاسن.( 8 (Gaza women pay the price of siege and aggression). Retrieved from https://www.aljazeera.net/news/2017/3/ ناودعلاو-راصحلا-نمث-نعفدي-ةزغ-ءاسن/ 8