Algorithmic Framing of Conflict: A Comparative Analysis of Google Gemini and ChatGPT in the Israeli–Palestinian Context
DOI:
https://doi.org/10.66815/ejecs/2640Keywords:
Palestine-Israeli conflict, AI representation, Text generated tools, large language models, Gemini, ChatGPTAbstract
This research examines the response of Google Geminia and ChatGPT to prompts related to the Israeli-Palestinian conflict by studying how AI technologies may reinforce or deepen existing discriminatory beliefs toward conflict, particularly being discriminatory towards Arabs. By applying a paired-prompt design, where each prompt is presented in two versions—one containing conflict-identifying entities (e.g., "Israel" and "Palestine") and one with these identities removed—this study disentangles moderation-driven refusals (e.g., safety and defamation policies) from representational patterns such as omission, framing, and keyword sensitivity. The research contributes a coding scheme for delivery status and discourse features, integrates ethical considerations for sensitive topics, and contextualizes findings within algorithmic fairness frameworks. The results indicate that Google Gemini had higher refusal rates for conflict-specific prompts than for variants removed. In contrast, ChatGPT generated more content overall but consistently enforced policies on sexual violence and unverified criminal allegations. The implications for AI auditing, responsible development, and culturally sensitive deployment in conflict-affected contexts are enormous.
Downloads
References
Abid, A., Farooqi, M., & Zou, J. (2021). Persistent anti-Muslim bias in large language models. Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 298–306. https://doi.org/10.1145/3442188.3445922 DOI: https://doi.org/10.1145/3461702.3462624
Almarashdi, H. S., Abu Khurma, O., AlArabi, K., Abulibdeh, E., & Yousef, J. (2026). AI-enhanced STEM education: A bibliometric study of research trends toward achieving sustainable development goals. European Journal of STEM Education, 11(1), 16. https://doi.org/10.20897/ejsteme/18190 DOI: https://doi.org/10.20897/ejsteme/18190
Badr, H. (2023). Artificial intelligence fails in the objectivity test. AJNET. https://is.gd/1RDxFz
Binns, R. (2018). Fairness in machine learning: Lessons from political philosophy. Proceedings of the 2018 Conference on Fairness, Accountability, and Transparency, 149–159. https://doi.org/10.1145/3287560.3287598
Birhane, A. (2021). Algorithmic injustice: A relational ethics approach. Patterns, 2(2), 100205. https://doi.org/10.1016/j.patter.2021.100205 DOI: https://doi.org/10.1016/j.patter.2021.100205
Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., … Liang, P. (2021). On the opportunities and risks of foundation models. arXiv. https://doi.org/10.48550/arXiv.2108.07258
Chen, Z. (2023). Ethics and discrimination in artificial intelligence-enabled recruitment practices. Humanities and Social Sciences Communications, 10, 2079. https://doi.org/10.1057/s41599-023-02079-x DOI: https://doi.org/10.1057/s41599-023-02079-x
Cramer, F., & Røed, S. (2025). Technological effects on gender studies: An intersectional perspective. Asia Pacific Journal of Education and Society, 14(1), 6. https://doi.org/10.20897/apjes/17975 DOI: https://doi.org/10.20897/apjes/17975
Crawford, K., & Paglen, T. (2021). Excavating AI: The politics of training sets for machine learning. AI & Society, 36(4), 1105–1116. https://doi.org/10.1007/s00146-021-01162-8 DOI: https://doi.org/10.1007/s00146-021-01301-1
Diakopoulos, N. (2019). Automating the news: how algorithms are rewriting the media. Harvard University Press. DOI: https://doi.org/10.4159/9780674239302
Diaz, A. (2008). Through the Google goggles: sociopolitical bias in search engine design. In A. Spink & M. Zimmer (Eds.). Web search. Information science and knowledge management (vol. 14, pp.11–34). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75829-7_2 DOI: https://doi.org/10.1007/978-3-540-75829-7_2
Germain, T. (2024, November 1). The 'bias machine': How Google tells you what you want to hear. BBC. https://www.bbc.com/future/article/20241031-how-google-tells-you-what-you-want-to-hear
Gillespie, T. (2020). Content moderation, AI, and the question of scale. Big Data & Society, 7(2), 1–5. https://doi.org/10.1177/2053951720943234 DOI: https://doi.org/10.1177/2053951720943234
Heinrichs, B. (2021). Discrimination in the age of artificial intelligence. AI & Society, 37(1), 143–154. https://doi.org/10.1007/s00146-021-01192-2 DOI: https://doi.org/10.1007/s00146-021-01192-2
Işıklı, Ş., & Fazlıoğlu, E. F. (2026). Technological effects on gender studies: An intersectional perspective. Feminist Encounters: A Journal of Critical Studies in Culture and Politics, 10(1), 23. https://doi.org/10.20897/femenc/17998 DOI: https://doi.org/10.20897/femenc/17998
Kalluri, P. (2020). Don't ask if artificial intelligence is good or fair, ask how it shifts power. Nature, 583(7815), 169. https://doi.org/10.1038/d41586-020-02003-2 DOI: https://doi.org/10.1038/d41586-020-02003-2
Kayhah, V.O. (2015). Confirmation bias: roles of search engines and search contexts [Conference presentation]. Thirty Sixth International Conference on Information Systems, Fort Worth, Texas, December 13-16. https://aisel.aisnet.org/icis2015/proceedings/HumanBehaviorIS/5/
Lakhani, M., & Khan, H. M. A. (2023). Fighting disinformation in the Palestine conflict: The role of generative AI and Islamic values. Al Misbah Research Journal, 3(4), 1–13. https://doi.org/10.5281/zenodo.11265514
Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, A., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., Newman, B., Raghunathan, A., Liang, P., & Hashimoto, T. (2022). Holistic evaluation of language models. Transactions on Machine Learning Research. https://openreview.net/forum?id=iO4LZibEqW
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys, 54(6), 1–35. https://doi.org/10.1145/3457607 DOI: https://doi.org/10.1145/3457607
Miskolczi, M. (2026). The illusion of reality: How AI-generated images (AIGIs) are fooling social media users. Computers in Human Behavior, 176, 108876. https://doi.org/10.1016/j.chb.2025.108876 DOI: https://doi.org/10.1016/j.chb.2025.108876
Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2), 1–21. https://doi.org/10.1177/2053951716679679 DOI: https://doi.org/10.1177/2053951716679679
Montaña-Niño, S. X. (2020). Automating the news: how algorithms are rewriting the media, Australian Journalism Review, 42(1), 139–140. https://doi.org/10.1386/ajr_00029_5 DOI: https://doi.org/10.1386/ajr_00029_5
Narayanan, A. (2018). Translation tutorial: 21 fairness definitions and their politics. Proceedings of the 2018 Conference on Fairness, Accountability, and Transparency, 1–5.https://facctconference.org/static/tutorials/narayanan-21defs18.pdf
Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism. NYU Press. https://nyupress.org/9781479837243/algorithms-of-oppression/
Oxford Analytica Daily Brief. (2023, November 9). Gaza war and GenAI test anti-disinformation efforts. https://doi.org/10.1108/OXAN-DB283260 DOI: https://doi.org/10.1108/OXAN-DB283260
Raji, I. D., Bender, E. M., Paullada, A., Denton, E., & Hanna, A. (2020). Saving face: Investigating the ethical concerns of facial recognition auditing. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 145–151. https://doi.org/10.1145/3375627.3375824 DOI: https://doi.org/10.1145/3375627.3375820
Selbst, A. D., Boyd, D., Friedler, S., Venkatasubramanian, S., & Vertesi, J. (2019). Fairness and abstraction in sociotechnical systems. Proceedings of the Conference on Fairness, Accountability, and Transparency, 59–68. https://doi.org/10.1145/3287560.3287598 DOI: https://doi.org/10.1145/3287560.3287598
Soufan Center. (2023, October 26). IntelBrief: AI-powered disinformation in the Israel–Hamas war and beyond. https://thesoufancenter.org/intelbrief-2023-october-26/
Sultan, Y., Dautova, G., & Dalle, J. (2025). Examining the relationship among artificial intelligence literacy, cultural literacy, and intercultural communication proficiency of philology students. Journal of Ethnic and Cultural Studies, 12(5), 345–362. https://doi.org/10.29333/ejecs/2839 DOI: https://doi.org/10.29333/ejecs/2839
Suzor, N. (2019). Lawless: The secret rules that govern our digital lives. Cambridge University Press.https://www.cambridge.org/core/books/lawless/8504E4EC8A74E539D701A04D3EE8D8DE
Tufekci, Z. (2015). Algorithmic harms beyond Facebook and Google: Emergent challenges of computational agency. Colorado Technology Law Journal, 13(2), 203–218. https://ctlj.colorado.edu/wp-content/uploads/2015/08/Tufekci-final.pdf
van Dijck, J., Poell, T., & de Waal, M. (2018). The platform society: Public values in a connective world. Oxford University Press. https://academic.oup.com/book/12378 DOI: https://doi.org/10.1093/oso/9780190889760.001.0001
Wachter, S., Mittelstadt, B., & Floridi, L. (2017). Why a right to explanation of automated decision-making does not exist in the General Data Protection Regulation. International Data Privacy Law, 7(2), 76–99. https://doi.org/10.1093/idpl/ipx005 DOI: https://doi.org/10.1093/idpl/ipx005
Zuiderveen Borgesius, F. J. (2020). Strengthening legal protection against discrimination by algorithms and artificial intelligence. The International Journal of Human Rights, 24(10), 1572–1593. https://doi.org/10.1080/13642987.2020.1743976 DOI: https://doi.org/10.1080/13642987.2020.1743976
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Bassant M. Attia

This work is licensed under a Creative Commons Attribution 4.0 International License.