Decolonizing the Literary AI in the Age of LLMs and Digital Neocolonialism

Document Type : Original Research Paper

Author

English Department, Faculty of Foreign Languages and Literatures, University of Tehran

10.22034/spektrum.2026.565038.1054
Abstract
Large Language Models (LLMs) are usually considered neutral technological advancements. However, critical digital studies increasingly emphasize the need to challenge their potential to perpetuate colonial power structures in cyberspace. This paper argues that LLMs function as powerful apparatuses of digital neocolonialism. It aims to diagnose this phenomenon within the field of literary AI and to propose a decolonial framework for its future development. This study demonstrates how the protocols of extracting and processing data privilege Western epistemologies in a systematic manner. Then, it develops a conceptual framework for the praxis of decolonial AI based on the principles of reciprocity and epistemic justice. The analysis reveals that the extractivist data collection utilized by dominant LLMs treats cultural and linguistic data as territory for appropriation, privileging the Western literary canon and erasing marginalized languages and traditions. This has led to linguistic homogenization and epistemic injustice as well as the imposition of aesthetic standards of the global West. In response, the proposed decolonial framework has necessitated a paradigm shift from extraction to reciprocity, which involves community-led data governance. Furthermore, AI should be used as a collaborative, co-creative tool by literary writers and researchers. As a further decolonial step, Eurocentric evaluative criteria in this field must be reformed in concrete ways. The decolonial approach advanced in this paper, seeks to fundamentally reposition literary AI. The ultimate goal of this repositioning is to foster a pluriversal aesthetic and epistemic framework.

Keywords

Subjects


Abdalla, M., & Abdalla, M. (2021). The grey hoodie project: Big tobacco, big tech, and the threat to academic integrity. AI Ethics, 1(4), 1-13. DOI: 10.1145/3461702.3462563
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). DOI: 10.1145/3461702.3462624
Alenichev, A., Shaffer, J., Kingori, P. et al. ‘We can see a savage’: a Case Study of the Colonial Gaze in Generative AI Algorithms. AI & Soc (2025). DOI: 10.1007/s00146-025-02685-0
Anantrasirichai, N., & Bull, D. (2022). Artificial Intelligence in the Creative Industries: A Review. Artificial Intelligence Review, 55(1), 589-651. DOI: 10.1007/s10462-021-10039-7
Anderson, J. E. (2015). Indigenous Knowledge and Intellectual Property Rights. In International Encyclopedia of the Social & Behavioral Sciences: Second Edition (pp. 769-778). Elsevier Inc..
Bender, E. M. (2019). The #BenderRule: On Naming the Languages We Study and Why It Matters. The Gradient.
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the Dangers of Stochastic Parrots: Can Language Models be Too Big? Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610–623. DOI: 10.1145/3442188.3445922
Benjamin, R. (2019). Race after Technology: Abolitionist Tools for the New Jim Code. Polity Press.
Bermudez, L. A. (2022). Participatory Design: Tools and Techniques for Re-imagining Digital Transformations. Berkman Klein Center for Internet & Society. https://andreslombana.net/blog/2022/06/16/participatory-design-tools-and-techniques-for-re-imagining-digital-transformations/
Bird, S. (2020). Decolonising Speech and Language Technology. Proceedings of the 28th International Conference on Computational Linguistics, 3501–3511. DOI: 10.18653/v1/2020.coling-main.313
Birhane, A. (2021). Algorithmic Injustice: A Relational Ethics Approach. Patterns, 2(2). DOI: 10.1016/j.patter.2021.100205
Birhane, A., Isaac, W. M., Prabhakaran, V., Díaz, M., Elish, M. C., Gabriel, I., & Mohamed, S. (2022). Power to the People? Opportunities and Challenges for Participatory AI. Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society, 272–282. DOI: 10.1145/3551624.3555290
Brookings Institution. (2024). Supporting a Community-Led Data Infrastructure to Build Local and Equitable Governance that Advances Policy. https://www.brookings.edu/articles/supporting-a-community-led-data-infrastructure-to-build-local-and-equitable-governance-that-advances-policy/
Bühler, M. M., Calzada, I., Cane, I., Jelinek, T., Kapoor, A., Mannan, M., Mehta, S., Mookerje, V., Nübel, K., Pentland, A., Scholz, T., Siddarth, D., Tait, J., Vaitla, B., & Zhu, J. (2023). Unlocking the Power of Digital Commons: Data Cooperatives as a Pathway for Data Sovereign, Innovative and Equitable Digital Communities. Digital3(3), 146-171. DOI: 10.3390/digital3030011
Couldry, N., & Mejias, U. A. (2019). The Costs of Connection: How Data is Colonizing Human Life and Appropriating it for Capitalism. In The Costs of Connection. Stanford University Press.
Crawford, K. (2021). Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. Yale University Press.
Daubs, M.S. (2025). Media Ensembles and Te Reo Māori (The Māori Language) In Aotearoa New Zealand. In: Manias-Muñoz, M., Bober, S., Willis, C. (eds) Minority Language Media. Palgrave Studies in Minority Languages and Communities. Palgrave Macmillan, Cham. DOI: 10.1007/978-3-031-71228-9_5
Delgado, F., Yang, S., Madaio, M., & Yang, Q. (2023). The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practice. Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, Article 347. DOI: 10.48550/arXiv.2310.00907
Della Ratta, D. (2025). What does Decolonising AI Really Mean? An Interview with Artist Ameera Kawash. Untold Mag. https://untoldmag.org/what-does-decolonising-ai-really-mean-an-interview-with-artist-ameera-kawash/
Dyer-Witheford, N., Kjosen, A. M., & Steinhoff, J. (2019). Inhuman Power: Artificial Intelligence and the Future of Capitalism. Pluto Press.
Emigh, R. J. (2024). Whither Digitality? The Relationship Between Orality, Literacy, and Digitality, Past and Present: From Spoken Traditions to Digital Media. Annual Review of Sociology, 50, 715-36. DOI: 10.1146/annurev-soc-033022-035644
Fricker, M. (2007). Epistemic Injustice: Power and the Ethics of Knowing. Oxford University Press.
Gramsci, A. (1971). Selections from the Prison Notebooks (Q. Hoare & G. N. Smith, Trans.). International Publishers.
Hansen, I. B., & Rafner, J. (2025). A Thematic Framework for Human-AI Co-creative Writing: Writers' Experiences of the Process. OSF.
Harvey, D. (2004). The 'New' Imperialism: Accumulation by Dispossession. Socialist Register, 40, 63-87. https://socialistregister.com/index.php/srv/article/view/5811.
Jones, PL., Mahelona, K., Duncan, S. et al. Kaitiaki: closing the door on open Indigenous data. Int J Digit Libr 26, 1 (2025). DOI: 10.1007/s00799-025-00410-2
Joshi, P., Santy, S., Budhiraja, A., Bali, K., & Choudhury, M. (2020). The State and Fate of Linguistic Diversity and Inclusion in the NLP World. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 6282–6293. Association for Computational Linguistics. DOI: 10.48550/arXiv.2004.09095
Karpouzis,  K. (2025). AI, Digital Humanities, and the Legacies of Colonial Power. Preprints. DOI: 10.20944/preprints202502.1823.v1
Kreutzer, J., Caswell, I., Wang, L., Wahab, A., van Esch, D., Ulzii-Orshikh, N., ... & Wu, S. (2022). Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets. Transactions of the Association for Computational Linguistics, 10, 50–72. DOI: 10.1162/tacl_a_00447
Kukutai, T., & Taylor, J. (Eds.). (2016). Indigenous Data Sovereignty: Toward an Agenda. ANU Press.
Lazem, S., Giglitto, D., Nkwo, M.S. et al. (2022). Challenges and Paradoxes in Decolonising HCI: A Critical Discussion. Comput Supported Coop Work, 31, 159–196. DOI: 10.1007/s10606-021-09398-0Liang, L. (2004). A Guide to Open Content Licenses. Piet Zwart Institute.
Mohamed, S., Png, MT. & Isaac, W. Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligence. Philos. Technol. 33, 659–684 (2020). DOI: 10.1007/s13347-020-00405-8
Moruzzi, C. (2025). Artificial Intelligence and Creativity. Philosophy Compass, 20: e70030. DOI: 10.1111/phc3.70030
Muldoon, J., Wu, B.A. Artificial Intelligence in the Colonial Matrix of Power. (2023). Philos. Technol. 36, 80. DOI: 10.1007/s13347-023-00687-8Phillipson, R. (1992). Linguistic Imperialism. Oxford University Press.
Qadri, R., Shelby, R., Bennett, C. L., & Denton, E. (2023). AI’s Regimes of Representation: A Community-Centered Study of Text-to-Image Models in South Asia. Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, 1605–1617. DOI: 10.48550/arXiv.2305.11844
Romaine, S. (2015). The Global Extinction of Languages and its Consequences for Cultural Diversity. In H. F. Marten, M. Rießler, J. Saarikivi, & R. Toivanen (Eds.), Cultural and Linguistic Minorities in the Russian Federation and the European Union (pp. 31-46). Springer. DOI: 10.1007/978-3-319-10455-3_2
Said, E. W. (1978). Orientalism. Pantheon Books.
Salehi, K., Habib Zadeh Khiyaban, S. and Sabbar, S. (2025). Artificial Intelligence and the Future of International Law and Power. Journal of World Sociopolitical Studies, 9(4), 923-958. doi: 10.22059/wsps.2025.401951.1552
Santos, B. d. S. (2014). Epistemologies of the South: Justice Against Epistemicide. Paradigm Publishers.
Shabanpour, M. B. (2025). Born-Digital Dialectics: Twitter Literature as a Cyberspace Genre. Research in Contemporary World Literature, 30(2), 695-732. DOI: 10.22059/jor.2025.400667.2711
Shelby, R., Rismani, S., Henne, K., Moon, A., Rostamzadeh, N., Nicholas, P. O., Yilla-Akbari, N., Gallegos, J., Smart, A., García, E. G., & Virk, G. (2023). Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction. arXiv. DOI: 10.48550/arXiv.2305.18247
Spivak, G. C. (1994). Can the Subaltern Speak? In P. Williams & L. Chrisman (Eds.), Colonial Discourse and Post-Colonial Theory: A Reader (pp. 66–111). Columbia University Press.
Thatcher, J., O'Sullivan, D., & Mahmoudi, D. (2016). Data Colonialism through Accumulation by Dispossession: New Metaphors for Daily Data. Environment and Planning D: Society and Space, 34(6), 990-1006. DOI: 10.1177/0263775816633195
Varshney, K. R. (2024). Decolonial AI Alignment: Openness, Visesa-Dharma, and Including Excluded Knowledges. In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, Vol. 7, pp. 1467-1481. DOI: 10.1609/aies.v7i1.31739
Weinberg, L. (2022). Rethinking Fairness: An Interdisciplinary Survey of Critiques of Hegemonic ML Fairness Approaches. Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society, 1005–1014. DOI: 10.1145/3514094.3534145
Whittle, S. (2025) Digital Chaucer Pedagogy and Editing: Probing Generative AI’s Reproduction of Hegemony. Scholarly Editing: The Annual of the Association for Documentary Editing, 42. DOI: 10.55520/ZXSXERGH
Wright, D., Masud, S., Moore, J., Yadav, S., Antoniak, M., Christensen, P. E., ... & Augenstein, I. (2025). Epistemic Diversity and Knowledge Collapse in Large Language Models. arXiv preprint arXiv:2510.04226. DOI: 10.48550/arXiv.2510.04226
Zuboff, S. (2023). The Age of Surveillance Capitalism. In Social Theory Re-Wired (pp. 203-213). Routledge.

Articles in Press, Accepted Manuscript
Available Online from 23 February 2026