ChatGPT’s interpretation of contested memoryscapes favours the voice of current governments, capitalism and the far-right
Open accessAbstract
This study examines political biases in ChatGPT-4o’s interpretations of memoryscapes linked to dictatorial regimes in Spanish-speaking countries. Using a least-to-most prompting model and a Discourse-Historical Approach (DHA), we analysed responses in both Spanish and English about three key memorial sites: the Valley of the Fallen (Spain), the ESMA Museum and Site of Memory (Argentina), and the Monument to Salvador Allende (Chile). Findings show a preference for dominant, capitalist, and government-aligned perspectives, often reinforced by far-right rhetoric, particularly in Spanish language. The research highlights generative AI’s influence in shaping heritage narratives and the challenges of ensuring unbiased and multi-vocal outputs. This study advances the fields of cultural heritage, digital humanities, and education, while also shedding light on the wider implications of ChatGPT’s outputs for memory politics and democratic governance.
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Algorithmic systems often privilege dominant cultural narratives, altering the meanings consumers encounter and the products they see.
“Other work finds that algorithmic systems often privilege dominant cultural narratives, altering the meanings consumers encounter and the products they see (González-Cantera & Bonacchi, 2025).”
From Marketing to the Machine: Rethinking Global Marketing for the AI Era (2026) · cites this paper as González-Cantera & Bonacchi, 2025
The indexed text (section “1 Active”; no page given) reports that ChatGPT-4o’s interpretations of three contested memorial sites favoured dominant, capitalist, and government-aligned perspectives, supporting the point about privileged narratives. It does not address consumers or the products they see, and the finding is specific to ChatGPT’s heritage interpretations rather than algorithmic systems generally. (LLM, conf=0.85)
Experimental — accuracy not yet measuredResearch on model-based curation shows that models apply their own interpretive frames, often prioritizing dominant narratives or reducing complex cultural categories to simplified tropes.
“Research on model-based curation shows how models apply their own interpretive frames, often prioritizing dominant narratives or reducing complex cultural categories to simplified tropes (González-Cantera & Bonacchi, 2025).”
From Marketing to the Machine: Rethinking Global Marketing for the AI Era (2026) · cites this paper as González-Cantera & Bonacchi, 2025
The paper’s title identifies a study of ChatGPT interpreting contested memoryscapes and favouring the voices of current governments, capitalism and the far right. That supports the point about dominant narratives, but the passages available from our copy do not establish the claim about simplifying complex cultural categories into tropes or about model-based curation generally (searched: simplified tropes, cultural categories, dominant narratives). (LLM, conf=0.78)
Experimental — accuracy not yet measuredAt the representational level, generative systems actively construct category logics and evaluative frames.
“At the representational level, generative systems actively construct category logics and evaluative frames, amplifying concerns about synthetic content, hallucination, and narrative distortion raised in emerging AI research (Campbell et al., 2022;González-Cantera & Bonacchi, 2025;Spennemann, 2023).”
From Marketing to the Machine: Rethinking Global Marketing for the AI Era (2026) · cites this paper as González-Cantera & Bonacchi, 2025
The passage labeled “1 Active” (page unavailable) says ChatGPT-4o’s interpretations of three memorial sites favour dominant, capitalist, and government-aligned perspectives and influence heritage narratives. That supports the claim about evaluative framing, but the available passages do not establish that it constructs category logics or address synthetic content and hallucination. (LLM, conf=0.78)
Experimental — accuracy not yet measuredTraining data carry the cultural assumptions, interpretive frames, and semantic patterns of their sources, which can privilege certain meanings and downplay others.
“Training data carry the cultural assumptions, interpretive frames, and semantic patterns of their sources, which can privilege certain meanings and downplay others (González-Cantera & Bonacchi, 2025).”
From Marketing to the Machine: Rethinking Global Marketing for the AI Era (2026) · cites this paper as González-Cantera & Bonacchi, 2025
The retrieved passages contain bibliographic material and an abstract, but no full-text passage addressing whether training data carry cultural assumptions, interpretive frames, or semantic patterns. Searches for those terms and for training-dataset bias did not locate a passage supporting the attributed claim. (LLM, conf=0.70)
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Cites
On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445922 · Cited by 6,988 · cited 2× · see citing works
“These tools are compared to ‘parrots’ (Bender et al. 2021), as they replicate language without grasping its meaning.”
Survey of Hallucination in Natural Language Generation
Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y. J., Madotto, A., & Fung, P. (2023) · ACM Computing Surveys · doi:10.1145/3571730 · Cited by 5,025 · cited 2× · see citing works
“This is because GenAI can experience a phenomenon called ‘hallucination’ (Alkaissi and McFarlane 2023; Ji et al. 2023), generating incorrect or misleading answers.”
What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education
Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., & Agyemang, B. (2023) · Smart Learning Environments · doi:10.1186/s40561-023-00237-x · Cited by 2,003 · cited 2× · see citing works
“They prioritise fulfilling prompts (Tlili et al. 2023), producing responses that align with user expectations, corporate interests, or prevailing narratives (Munn, Magee, and Arora 2023, 12–13).”
Holistic Evaluation of Language Models
Bommasani, R., Liang, P., & Lee, T. (2023) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.15007 · Cited by 480 · see citing works
More human than human: measuring ChatGPT political bias
Motoki, F., Pinho Neto, V., & Rodrigues, V. (2024) · Public Choice · doi:10.1007/s11127-023-01097-2 · Cited by 329 · see citing works
Truth machines: synthesizing veracity in AI language models
Munn, L., Magee, L., & Arora, V. (2024) · AI & SOCIETY · doi:10.1007/s00146-023-01756-4 · Cited by 65 · see citing works
Linguistic Bias in ChatGPT: Language Models Reinforce Dialect Discrimination
Fleisig, E., Smith, G., Bossi, M., Rustagi, I., Yin, X., & Klein, D. (2024) · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2024.emnlp-main.750 · Cited by 64 · see citing works
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Marketing to the Machine: Rethinking Global Marketing for the AI Era
Sands, S., & Campbell, C. (2026) · Journal of Global Marketing · doi:10.1080/08911762.2026.2697021
“Research on model-based curation shows how models apply their own interpretive frames, often prioritizing dominant narratives or reducing complex cultural categories to simplified tropes (González-Cantera & Bonacchi, 2025).”
- Authors
- González-Cantera, T., & Bonacchi, C.
- Affiliations
- Universitat de Girona (ES)
- University of Edinburgh (GB)
- Corresponding author
- González-Cantera, Tania
- Venue
- International Journal of Heritage Studies
- Publisher
- Informa UK Limited
- Published
- 2025-08-29
- Volume
- 31
- Issue
- 11
- Pages
- 1508-1530
- ISSN
- 1352-7258 (ISSN-L)
- 1470-3610
- OpenAlex
- W4413817875
- Cited by
- 4
- References
- 7
- Citations by year
- 2026: 3 · 2025: 1
- FWCI
- 4.43
- Type
- journal article
- Language
- en
- Open access
- Licence
- cc-by
- Locations
- International Journal of Heritage Studies (publishedVersion)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, cc-by)
- Funders
- la Caixa” Foundation (LCF/BQ/DR21/11880004)
- The Leverhulme Trust (PLP-2022-297)
- Topics
- Social Sciences › Psychology › Social Psychology › Memory, Trauma, and Commemoration
- Social Sciences › Psychology › Social Psychology › Memory, violence, and history
- Social Sciences › Arts and Humanities › Archeology › Cultural Identity and Heritage
- Keywords
- GPT-4o, memory politics, memorial sites, Valley of the Fallen, far-right rhetoric, capitalism, Spanish-speaking countries, least-to-most prompting, discourse-historical approach
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