SavedAdd your journal

Journals / Göç Araştırmaları Dergisi / 2026 / Cilt: 12 Sayı: 1 (Göç ve Yapay Zekâ) Özel Sayı

THE IMAGE OF IMMIGRANTS IN THE AGE OF GENERATIVE AI: A DISCUSSION ON THE GATEKEEPING ROLE OF ALGORITHMS IN IMMIGRANT DEPICTION

ÜRETKEN YAPAY ZEKÂ ÇAĞINDA GÖÇMEN İMGESİ: GÖÇMEN TASVİRİNDE ALGORİTMALARIN EŞİK BEKÇİLİĞİ ÜZERİNE BİR TARTIŞMA

Melikşah Aksu(Yeditepe Üniversitesi)0009-0006-2791-1543
Oğuz Kuş(İstanbul Üniversitesi)0000-0002-2593-4980
Pages
226–246
Publication Date
2026-08-06
🏛️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)

In this context, it is believed that expanding the experimental research included in this study to increase awareness of these risks and improve information literacy will illuminate the information pathway at the intersection of migration and artificial intelligence.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Background: The rapid increase in the use of generative AI tools today has led to an acceleration of AI-generated content within the information ecosystem.
📊
Key Findings & Data:Many concrete examples from the past show that AI systems, powered by algorithms, present biased portrayals in imagery concerning disadvantaged groups.
💡
Conclusion & Impact:In this context, it is believed that expanding the experimental research included in this study to increase awareness of these risks and improve information literacy will illuminate the information pathway at the intersection of migration and artificial intelligence.
🔬Design:Derleme
📍Scope:Comprehensive Sample
⚙️Variables:Algoritma · Eşik bekçiliği · Göç araştırmaları

Abstract

Background: The rapid increase in the use of generative AI tools today has led to an acceleration of AI-generated content within the information ecosystem. Many concrete examples from the past show that AI systems, powered by algorithms, present biased portrayals in imagery concerning disadvantaged groups. Therefore, the question arises as to how algorithm-based generative AI systems will act as gatekeepers in shaping societal perceptions of immigrant populations, which constitute the largest quantitative base among disadvantaged groups. Objective: This study aims to discuss the potential impact of algorithm-based generative AI systems on the construction of societal perceptions of immigrants, who constitute the largest segment of disadvantaged groups, considering existing literature and concrete examples. Method: The study is based on a conceptual research approach, and the discussion is conducted in light of prior literature and concrete examples. Finding: Numerous research studies have shown that generative AI tools transfer the tradition of stereotyping borrowed from traditional media into algorithms when depicting migrants. These depictions often include stereotypes of Arab, African, and Latin American races, particularly in unsettling situations. As a result of these findings, generative AI systems are concluded to fuel anti-immigrant sentiment in society and pave the way for the acceleration of anti-immigrant far-right regimes. Conclusion: The study concludes that generative AI systems, in their depictions of migrants, act as gatekeepers producing biased results, potentially posing certain societal risks by undermining cultural diversity. In this context, it is believed that expanding the experimental research included in this study to increase awareness of these risks and improve information literacy will illuminate the information pathway at the intersection of migration and artificial intelligence.

Özet

Arka Plan: Günümüzde üretken yapay zekâ araçlarının kullanımındaki yoğun artış, bilgi ekosistemi içerisindeki yapay zekâ tarafından üretilen içeriklerin ivme kazanmasına yol açmıştır. Geçmişteki birçok somut örnek, algoritmalar tarafından beslenen yapay zekâ sistemlerinin dezavantajlı grupları konu alan imgelemelerde yanlı tasvirler sunduğunu göstermektedir. Buradan hareketle dezavantajlı grupların en yoğun popülasyonlarından birisini teşkil eden göçmen nüfusuna yönelik toplumsal tasavvurun oluşumunda, üretken yapay zekâ sistemlerinin eşik bekçiliği rolünü ne şekilde üstleneceği merak konusu olmuştur. Amaç: Çalışma, algoritmik tabanlı üretken yapay zekâ sistemlerinin, dezavantajlı gruplar içerisinde niceliksel bir baskınlığa sahip olan göçmenlere yönelik toplumsal algıların inşasındaki potansiyel etkisini mevcut literatür ve somut örnekleri göz önünde bulundurarak tartışmayı amaçlamaktadır. Yöntem: Çalışma ekseninde kavramsal araştırma konsepti göz önünde bulundurularak, öncül literatür ve somut örnekler ışığında tartışma gerçekleştirilmiştir. Bulgular: Üretken yapay zekâ araçlarının göçmen tasvirlerinde geleneksel medyadan ödünç alınan stereotipleştirme geleneğinin algoritmalara aktarıldığı, göçmen tasvirlerinde özellikle güvenilir olmayan vaziyetler içerisinde Arap, Afrikalı, Latin Amerikalı bireylerin klişeleşmiş görüntülerine yer verildiği tespit edilen birçok araştırmaya kavramsal çerçevede değinilmiştir. Söz konusu kavramsal tartışma neticesinde üretken yapay zekâ sistemlerinin toplumdaki göçmen karşıtlığını beslediği ve göçmen karşıtı aşırı sağcı rejimlerin ivmelenmesine zemin hazırladığı gözlemlenmektedir. Sonuç: Çalışma sonucunda üretken yapay zekâ sistemlerinin, göçmen tasvirlerinde yanlı sonuçlar üreten bir eşik bekçiliği rolü üstlenmesinin toplumsal açıdan belirli riskleri beraberinde getirme potansiyeline sahip olduğu görülmüştür. Söz konusu riskler bağlamında çalışma kapsamında yer verilen deneysel araştırmaların yaygınlaştırılarak bilgi okuryazarlığı noktasında farkındalığın geliştirilmesinin, göçmen ve yapay zekâ kesişimindeki bilgi güzergahını aydınlatacağı düşünülmektedir.

🔖

Cite This Article

Aksu, M., Kuş, O. (2026). THE IMAGE OF IMMIGRANTS IN THE AGE OF GENERATIVE AI: A DISCUSSION ON THE GATEKEEPING ROLE OF ALGORITHMS IN IMMIGRANT DEPICTION. Göç Araştırmaları Dergisi, Cilt: 12 Sayı: 1 (Göç ve Yapay Zekâ) Özel Sayı, 226–246. https://doi.org/10.70972/gad.1908409

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (64)

  1. Ahmed, S., Jaidka, K., Chen, V. H. H., Cai, M., Chen, A., Emes, C. S., Yu, V. & Chib, A. (2024). Social media and anti-immigrant prejudice: a multi-method analysis of the role of social media use, threat perceptions, and cognitive ability. Frontiers in psychology, 15. https://doi.org/10.3389/fpsyg.2024.1280366.
  2. Amores, J. J., Arcila-Calderón, C., & González-de-Garay, B. (2020). The gendered representation of refugees using visual frames in the main Western European media. Gender issues, 37(4), 291-314. https://doi.org/10.1007/s12147-020-09248-1
  3. Arora, A., Barrett, M., Lee, E., Oborn, E., & Prince, K. (2023). Risk and the future of AI: Algorithmic bias, data colonialism, and marginalization. Information and Organization, 33(3). https://doi.org/10.1016/j.infoandorg.2023.100478
  4. Bakshy, E., Messing, S., & Adamic, L. A. (2015). Exposure to ideologically diverse news and opinion on Facebook. Science, 348(6239), 1130-1132. https://doi.org/10.1126/science.aaa1160
  5. Bañuelos Capistrán, J. (2026). AI-generated visual imaginaries: Migration, activism and social resistance at the US-Mexico border. In Media, Migrants, and US Border (s) (pp. 201-218). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-032-00465-9_12
  6. Barisione, M., Michailidou, A., & Airoldi, M. (2019). Understanding a digital movement of opinion: the case of #RefugeesWelcome. Information, Communication & Society, 22(8), 1145–1164. https://doi.org/10.1080/1369118X.2017.1410204
  7. Broderick L., Turner, E., & Mas, M. (2025). Race and culture AI is not neutral. Re Vista. https://revista.drclas.harvard.edu/race-and-culture-ai-is-not-neutral
  8. Buarque, B. (2025). The Weaponisation of AI: Visual storytelling of the Great Replacement Conspiracy Theory Amid the Southport Riots. Global Network. https://gnet-research.org/2025/07/16/the-weaponisation-of-ai-visual-storytelling-of-the-great-replacement-conspiracy-theory-amid-the-southport-riots
  9. Campo-Ruiz, I. (2025). Artificial intelligence may affect diversity: architecture and cultural context reflected through ChatGPT, Midjourney, and Google Maps. Humanities and Social Sciences Communications, 12(1), 24. https://doi.org/10.1057/s41599-024-03968-5
  10. Castells, M. (2012). Networks of outrage and hope: Social movements in the Internet age. Polity.
  11. Castillo, S. M. (2025). Algorithmic fairness in digital governance: Mitigating bias for marginalized groups in Latin America. Global Pollution Solutions, 1(1), 43-56.
  12. Cottle, S. (2007). Mediatised recognition and the ‘other’. Media International Australia, 123(1), 34-48. https://doi.org/10.1177/1329878X0712300105
  13. Couldry, N. (2008). Media and the problem of voice. Participation and media production: Critical reflections on content creation, 15-26.
  14. De Haas, H., Castles, S., & Miller, M. J. (2020). The age of migration: International population movements in the modern world. Red Globe Press.
  15. Doshi, A. R., & Hauser, O. P. (2024). Generative AI enhances individual creativity but reduces the collective diversity of novel content. Science Advances, 10(28). https://doi.org/10.1126/sciadv.adn5290
  16. Dsouza, N. (2025). Comparative Analysis of leading generative AI conversational systems: ChatGPT, Grok AI, Gemini, and Meta AI. Authorea Preprints. https://doi.org/10.36227/techrxiv.175393328.84629021/v1
  17. Duffy, B. E., & Meisner, C. (2023). Platform governance at the margins: Social media creators’ experiences with algorithmic (in) visibility. Media, Culture & Society, 45(2), 285-304. https://doi.org/10.1177/01634437221111923
  18. Fawcett, S. E., Waller, M. A., Miller, J. W., Schwieterman, M. A., Hazen, B. T., & Overstreet, R. E. (2014). A trail guide to publishing success: tips on writing influential conceptual, qualitative, and survey research. Journal of Business Logistics, 35(1), 1-16. https://doi.org/10.1111/jbl.12039
  19. Fazelpour, S., & Danks, D. (2021). Algorithmic bias: Senses, sources, solutions. Philosophy Compass, 16(8). https://doi.org/10.1111/phc3.12760
  20. Ferrer, A. F., & Retis, J. (2019). Ethnic minority media: Between hegemony and resistance. Journal of Alternative & Community Media, 4(3), 1-13. https://doi.org/10.1386/joacm_00054_1
  21. Frizzera, L. (2018). I/O: Reinforcing newsmaking practices through algorithmic media. Stream: Interdisciplinary Journal of Communication, 10(1), 39-51. https://doi.org/10.21810/strm.v10i1.251
  22. Fürsich, E. (2010). Media and the representation of others. International Social Science Journal, 61(199), 113-130. https://doi.org/10.1111/j.1468-2451.2010.01751.x
  23. Georgiou M (2012) Introduction: gender, migration and the media. Ethnic and Racial Studies 35(5): 791-799. https://doi.org/10.1080/01419870.2011.628041
  24. Gillespie, T. (2014). The relevance of algorithms. In: T. Gillespie, P. J. Boczkowski, & K. A. Foot (Eds.). Media Technologies: Essays on Communication, Materiality, and Society, pp. 167–194. MIT Press.
  25. Gillespie, T. (2018). Custodians of the Internet: Platforms, content moderation, and the hidden decisions that shape social media. Yale University Press.
  26. Goldhamer, M. & Chopra, A. (2026). ‘Misrepresent reality’: AI-altered shooting image surfaces in U.S. Senate. The Japan Times. https://www.japantimes.co.jp/news/2026/02/01/world/politics/ai-altered-image-us-senate
  27. Gonçalves, I. (2024). Promoting hate speech by dehumanizing metaphors of immigration. Journalism Practice, 18(2), 265-282. https://doi.org/10.1080/17512786.2023.2212661
  28. Growcoot M. (2025). Google’s Nano Banana Pro AI model: Further erodes trust in photos. PetaPixel. https://petapixel.com/2025/11/26/googles-nano-banana-pro-ai-model-further-erodes-trust-in-photos
  29. Hall, S. (1997). The spectacle of the other. Representation: Cultural representations and signifying practices, 7, 223-290.
  30. Hjarvard, S. (2008). The mediatization of society: A theory of the media as agents of social and cultural change. Nordicom review, 29(2). https://doi.org/10.1515/nor-2017-0181
  31. Jakesch, M., Hancock, J. T., & Naaman, M. (2023). Human heuristics for AI-generated language are flawed. Proceedings of the National Academy of Sciences, 120(11), https://doi.org/10.1073/pnas.2208839120.
  32. Kaneti, M. (2023). What can AI see? The image of the ‘migrant’in the era of AI post-visualization. Journal of Global Ethics, 19(3). https://doi.org/10.1080/17449626.2023.2279229
  33. Larson, J., Mattu, Kirchner L. & Angwin J. (2016). How we analyzed the COMPAS recidivism algorithm. Pro Publica. https://www.propublica.org/article/how-we-analyzed-the-compas-recidivism-algorithm
  34. Lavikainen, J. (2023). Russia’s hybrid operation at the Finnish border: Using migrants as a tool of influence. FIIA. https://fiia.fi/en/publication/russias-hybrid-operation-at-the-finnish-border
  35. LoDolce, M. & Howley, C. (2024). Gartner predicts search engine volume will drop 25% by 2026, due to AI chatbots and other virtual agents. Gartner. https://www.gartner.com/en/newsroom/press-releases/2024-02-19-gartner-predicts-search-engine-volume-will-drop-25-percent-by-2026-due-to-ai-chatbots-and-other-virtual-agents
  36. Lucht, H., Cold-Ravnkilde, S.M. & Larsen, K.P. (2026). The instrumentalisation of migration: ‘Hybrid warfare’ at Europe's external borders? DIIS. https://www.diis.dk/en/event/the-instrumentalisation-of-migration-hybrid-warfare-europes-external-borders
  37. Magder, T. (2004). Transnational media, international trade and the idea of cultural diversity. Continuum, 18(3). https://doi.org/10.1080/1030431042000256126
  38. Marchlewska, M., Cichocka, A., Łozowski, F., Górska, P., & Winiewski, M. (2019). In search of an imaginary enemy: Catholic collective narcissism and the endorsement of gender conspiracy beliefs. The Journal of Social Psychology, 159(6). https://doi.org/10.1080/00224545.2019.1586637
  39. Munoriyarwa, A., & de-Lima-Santos, M. F. (2025). Generative AI and the future of news: Examining AI’s agency, power, and authority. Journalism Practice, 19(10). https://doi.org/10.1080/17512786.2025.2545448
  40. Myers West, S. (2018). Censored, suspended, shadowbanned: User interpretations of content moderation on social media platforms. New Media & Society, 20(11). https://doi.org/10.1177/1461444818773059
  41. Nail, T. (2015). The figure of the migrant. Stanford University Press.
  42. Navaz, L. S., & Ferrer, A. F. (2012). Migrant minority media: Towards a democratization of the Western mediascape?. In Mediating cultural diversity in a globalized public space (pp. 76-95). Palgrave. https://doi.org/10.1057/9781137283405_5
  43. Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism. In Algorithms of oppression. New York University Press. https://doi.org/10.18574/nyu/9781479833641.001.0001
  44. Nyaaba, M., Wright, A. L., & Choi, G. L. (2026). Generative AI and digital neocolonialism in global education: Towards an equitable framework. Journal of Digital Educational Technology, 6(1). https://doi.org/10.30935/jdet/17862
  45. Olaru, G. O. (2023). The rapid diffusion of fake news: An analysis of content on migration, refugees, and conflict on international fact-checking platforms. Connectist: Istanbul University Journal of Communication Sciences, (65), 61-87. https://doi.org/10.26650/CONNECTIST2023-1404666
  46. Olha, B. (2023). Threads and upgrades of AI technology in the new media era. Grail of Science. https://doi.org/10.36074/grail-of-science.17.02.2023.080
  47. Padalko, H. (2025). AI and information manipulation: Russia’s interference in the US elections. Centre for International Governance Innovation.
  48. Pariser, E. (2011). The filter bubble: What the Internet is hiding from you. Penguin UK.
  49. Park, T. J., & Rohatgi, A. (2024). Balancing the platform responsibility paradox: A case for amplification regulation to mitigate the spread of harmful but legal content online. Computer Law & Security Review, 52, https://doi.org/10.1016/j.clsr.2024.105960.
  50. Pietrandrea, P., & Battaglia, E. (2022). “Migrants and the EU”. The diachronic construction of ad hoc categories in French far-right discourse. Journal of Pragmatics, 192, 139-157. https://doi.org/10.1016/j.pragma.2022.02.012
  51. Rodríguez-Pose, A., Terrero-Dávila, J., & Lee, N. (2023). Left-behind versus unequal places: interpersonal inequality, economic decline and the rise of populism in the USA and Europe. Journal of Economic Geography, 23(5), 951-977. https://doi.org/10.1093/jeg/lbad005
  52. Scheel, S. (2019). Autonomy of migration?: Appropriating mobility within biometric border regimes. Routledge. https://doi.org/10.1093/sf/soaa060
  53. Shankar, P., Dixit, P. & Siddiqui U. (2023) Are social media giants censoring pro-Palestine voices amid Israel’s war? Al Jazeera. https://www.aljazeera.com/features/2023/10/24/shadowbanning-are-social-media-giants-censoring-pro-palestine-voices
  54. Sharma, N. (2020). Home rule: National sovereignty and the separation of natives and migrants. Duke University Press. https://doi.org/10.1093/sf/soaa060
  55. Shoemaker, P. J., & Reese, S. D. (2013). Mediating the message in the 21st century: A media sociology perspective. Routledge.
  56. Shoemaker, P. J., & Vos, T. (2009). Gatekeeping theory. Routledge.
  57. Simon, F., Nielsen, K.N. & Fletcher, R. (2025). Generative AI and news report 2025: How people think about AI’s role in journalism and society. Reuters Institute. https://reutersinstitute.politics.ox.ac.uk/generative-ai-and-news-report-2025-how-people-think-about-ais-role-journalism-and-society
  58. Tomlinson, J. (2001). Cultural imperialism: A critical introduction. A&C Black.
  59. Tong, A. Wang E. & Coulter, M. (2024). Exclusive: Reddit in AI content licensing deal with Google. Reuters. https://www.reuters.com/technology/reddit-ai-content-licensing-deal-with-google-sources-say-2024-02-22
  60. Tufekci, Z. (2008). Can you see me now? Audience and disclosure regulation in online social network sites. Bulletin of Science, Technology & Society, 28(1), 20-36. https://doi.org/10.1177/0270467607311484
  61. UNESCO. (2013). Global Media and Information Literacy Assessment Framework: Country Readiness and Competencies. United Nations Educational, Scientific and Cultural Organization.
  62. Van Dalen, A. (2023). Algorithmic gatekeeping for figürefional communicators: Power, trust, and legitimacy. Routledge. https://doi.org/10.4324/9781003375258
  63. Whitten-Woodring, J., Kleinberg, M. S., Thawnghmung, A., & Thitsar, M. T. (2020). Poison if you don’t know how to use it: Facebook, democracy, and human rights in Myanmar. The International Journal of Press/Politics, 25(3), 407-425. https://doi.org/10.1177/1940161220919666
  64. Zolberg, A. R., & Benda, P. M. (2001). Global migrants, global refugees: Problems and solutions. Berghahn Books.