CoChat
Sign Up
Cited by 15Ethics and Social Impacts of AINatural Language Processing TechniquesSpeech and dialogue systems

The AI Gap: How Socioeconomic Status Affects Language Technology Interactions

Open access
Bassignana, Elisa, Curry, Amanda Cercas, Hovy, Dirk·2025·Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
DOI 10.18653/v1/2025.acl-long.914
Open this paper in CoChat — free accountOpen PDF

Abstract

Socioeconomic status (SES) fundamentally influences how people interact with each other and more recently, with digital technologies like Large Language Models (LLMs).While previous research has highlighted the interaction between SES and language technology, it was limited by reliance on proxy metrics and synthetic data.We survey 1,000 individuals from diverse socioeconomic backgrounds about their use of language technologies and generative AI, and collect 6,482 prompts from their previous interactions with LLMs.We find systematic differences across SES groups in language technology usage (i.e., frequency, performed tasks), interaction styles, and topics.Higher SES entails a higher level of abstraction, convey requests more concisely, and topics like 'inclusivity' and 'travel'.Lower SES correlates with higher anthropomorphization of LLMs (using "hello" and "thank you") and more concrete language.Our findings suggest that while generative language technologies are becoming more accessible to everyone, socioeconomic linguistic differences still stratify their use to exacerbate the digital divide.These differences underscore the importance of considering SES in developing language technologies to accommodate varying linguistic needs rooted in socioeconomic factors and limit the AI Gap across SES groups.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.18653/v1/2025.acl-long.914

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Socioeconomic factors, especially family income and parental education, continue to be significant determinants of technological access and literacy.

    “Likewise, socioeconomic factors-especially family income and parental education-continue to be significant determinants of technological access and literacy (Bassignana et al., 2025).”

    From <b>Beyond Access: Socioeconomic, Academic and Disciplinary Determinants of Generative AI Use in Higher Education</b> (2026) · cites this paper as Bassignana et al., 2025

    Section 3 says socioeconomic status shapes access to technology and digital skills, and Section 6 reports significant differences in language-technology adoption and use across socioeconomic groups. The paper lists income as an SES factor and records parents’ education as demographic information, but the cited passages do not establish that family income or parental education specifically are significant determinants of access or literacy. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 42

  1. Gavin Abercrombie, Amanda Cercas Curry, Tanvi Dinkar, Verena Rieser, and Zeerak Talat. 2023. Mi- rages. on anthropomorphism in dialogue systems. In Proceedings of the 2023 Conference on Empir- ical Methods in Natural Language Processing, pages 4776-4790, Singapore. Association for Computa- tional Linguistics. · DOI · Paper page
  2. Nancy E. Adler, Elissa S. Epel, Grace Castellazzo, and Jeannette R. Ickovics. 2000. Relationship of subjec- tive and objective social status with psychological and physiological functioning: preliminary data in healthy white women. Health psychology : official journal of the Division of Health Psychology, Ameri- can Psychological Association, 19 6:586-92.
  3. Canfer Akbulut, Laura Weidinger, Arianna Manzini, Iason Gabriel, and Verena Rieser. 2024. All too human? mapping and mitigating the risk from an- thropomorphic ai. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 7(1):13-26. · DOI
  4. Basil Bernstein. 1960. Language and social class. The British Journal of Sociology, 11(3):271-276.
  5. Verena Blaschke, Christoph Purschke, Hinrich Schuetze, and Barbara Plank. 2024. What do dialect speakers want? a survey of attitudes towards language technol- ogy for German dialects. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 823-841, Bangkok, Thailand. Association for Computational Linguistics. · DOI
  6. Su Lin Blodgett, Jackie Chi Kit Cheung, Vera Liao, and Ziang Xiao. 2024. Human-centered evaluation of language technologies. In Proceedings of the 2024 Conference on Empirical Methods in Natural Lan- guage Processing: Tutorial Abstracts, pages 39-43, Miami, Florida, USA. Association for Computational Linguistics. · DOI
  7. Pierre Bourdieu. 1987. What makes a social class? on the theoretical and practical existence of groups. Berkeley journal of sociology, 32:1-17.
  8. Marc Brysbaert, Amy Beth Warriner, and Victor Ku- perman. 2014. Concreteness ratings for 40 thousand generally known english word lemmas. Behavior Research Methods, 46:904-911. · DOI · Paper page
  9. Tommaso Caselli, Roberto Cibin, Costanza Conforti, Enrique Encinas, and Maurizio Teli. 2021. Guid- ing principles for participatory design-inspired nat- ural language processing. In Proceedings of the 1st Workshop on NLP for Positive Impact, pages 27-35, Online. Association for Computational Linguistics. · DOI
  10. Amanda Cercas Curry, Giuseppe Attanasio, Zeerak Ta- lat, and Dirk Hovy. 2024a. Classist tools: Social class correlates with performance in NLP. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 12643-12655, Bangkok, Thailand. Association for Computational Linguistics. · DOI
  11. Amanda Cercas Curry, Zeerak Talat, and Dirk Hovy. 2024b. Impoverished language technology: The lack of (social) class in NLP. In Proceedings of the 2024 Joint International Conference on Compu- tational Linguistics, Language Resources and Eval- uation (LREC-COLING 2024), pages 8675-8682, Torino, Italia. ELRA and ICCL.
  12. Jianlyu Chen, Shitao Xiao, Peitian Zhang, Kun Luo, Defu Lian, and Zheng Liu. 2024. M3- embedding: Multi-linguality, multi-functionality, multi-granularity text embeddings through self- knowledge distillation. In Findings of the Asso- ciation for Computational Linguistics: ACL 2024, pages 2318-2335, Bangkok, Thailand. Association for Computational Linguistics. · DOI · Paper page
  13. Myra Cheng, Kristina Gligoric, Tiziano Piccardi, and Dan Jurafsky. 2024. AnthroScore: A computational linguistic measure of anthropomorphism. In Proceed- ings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Vol- ume 1: Long Papers), pages 807-825, St. Julian's, Malta. Association for Computational Linguistics.
  14. Madeleine IG Daepp and Scott Counts. 2024. The emerging ai divide in the united states. arXiv preprint arXiv:2404.11988. · arXiv
  15. Fred D Davis. 1989. Perceived usefulness, perceived ease of use, and user acceptance of information tech- nology. MIS quarterly, pages 319-340.
  16. DeepSeek-AI. 2024. Deepseek-v3 technical report. Preprint, arXiv:2412.19437. · arXiv
  17. Eustasio Del Barrio, Juan A Cuesta-Albertos, and Car- los Matrán. 2018. An optimal transportation ap- proach for assessing almost stochastic order. In The Mathematics of the Uncertain, pages 33-44. Springer.
  18. Jan Delcker, Joana Heil, Dirk Ifenthaler, Sabine Seufert, and Lukas Spirgi. 2024. First-year students ai- competence as a predictor for intended and de facto use of ai-tools for supporting learning processes in higher education. International Journal of Educa- tional Technology in Higher Education, 21(1):18.
  19. Rotem Dror, Segev Shlomov, and Roi Reichart. 2019. Deep dominance -how to properly compare deep neural models. In Proceedings of the 57th Annual Meeting of the Association for Computational Lin- guistics, pages 2773-2785, Florence, Italy. Associa- tion for Computational Linguistics. · DOI
  20. Nicholas Epley, Adam Waytz, and John T Cacioppo. 2007. On seeing human: a three-factor theory of an- thropomorphism. Psychological review, 114(4):864.
  21. Dirk Hovy, Shiri Melumad, and J Jeffrey Inman. 2021. Wordify: A Tool for Discovering and Differentiat- ing Consumer Vocabularies. Journal of Consumer Research, 48(3):394-414. · DOI
  22. Lujain Ibrahim, Saffron Huang, Lama Ahmad, and Markus Anderljung. 2024. Beyond static ai evaluations: advancing human interaction evalu- ations for llm harms and risks. arXiv preprint arXiv:2405.10632. · arXiv
  23. Hannah Rose Kirk, Alexander Whitefield, Paul Röttger, Andrew Michael Bean, Katerina Margatina, Rafael Mosquera, Juan Manuel Ciro, Max Bartolo, Adina Williams, He He, et al. 2024. The prism alignment dataset: What participatory, representative and indi- vidualised human feedback reveals about the subjec- tive and multicultural alignment of large language models. In The Thirty-eight Conference on Neural Information Processing Systems Datasets and Bench- marks Track.
  24. William Labov. 2006. The Social Stratification of En- glish in New York City, 2 edition. Cambridge Univer- sity Press.
  25. George Lakoff and Mark Johnson. 2008. Metaphors we live by. University of Chicago press.
  26. Martin le Vrang, Agis Papantoniou, Erika Pauwels, Pieter Fannes, Dominique Vandensteen, and Johan De Smedt. 2014. Esco: Boosting job matching in europe with semantic interoperability. Computer, 47(10):57-64. · DOI
  27. Heather Lent, Kelechi Ogueji, Miryam de Lhoneux, Ore- vaoghene Ahia, and Anders Søgaard. 2022. What a creole wants, what a creole needs. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 6439-6449, Marseille, France. Eu- ropean Language Resources Association.
  28. Q Vera Liao and Ziang Xiao. 2023. Rethinking model evaluation as narrowing the socio-technical gap. arXiv preprint arXiv:2306.03100. · arXiv
  29. Leland McInnes, John Healy, and Steve Astels. 2017. hdbscan: Hierarchical density based clustering. The Journal of Open Source Software, 2(11):205.
  30. Leland McInnes, John Healy, Nathaniel Saul, and Lukas Großberger. 2018. Umap: Uniform manifold ap- proximation and projection. Journal of Open Source Software, 3(29):861. · DOI · Paper page
  31. Nicolai Meinshausen and Peter Bühlmann. 2010. Stabil- ity selection. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 72(4):417-473. · DOI
  32. Farooq Mubarak, Reima Suomi, and Satu-Päivi Kantola. 2020. Confirming the links between socio-economic variables and digitalization worldwide: the unset- tled debate on digital divide. Journal of Information, Communication and Ethics in Society, 18(3):415- 430.
  33. OpenAI. 2023. Chatgpt: Large language model. https: //chat.openai.com. and the 9th International Joint Conference on Natu- ral Language Processing (EMNLP-IJCNLP), pages 3982-3992, Hong Kong, China. Association for Com- putational Linguistics.
  34. Manon Reusens, Philipp Borchert, Jochen De Weerdt, and Bart Baesens. 2024. Native design bias: Studying the impact of english nativeness on language model performance. arXiv preprint arXiv:2406.17385. · arXiv
  35. Mike Savage, Fiona Devine, Niall Cunningham, Mark Taylor, Yaojun Li, Johs Hjellbrekke, Brigitte Le Roux, Sam Friedman, and Andrew Miles. 2013. A new model of social class? findings from the bbc's great british class survey experiment. Sociology, 47(2):219-250. · DOI · Paper page
  36. Teresa Scantamburlo, Atia Cortés, Francesca Foffano, Cristian Barrué, Veronica Distefano, Long Pham, and Alessandro Fabris. 2024. Artificial intelligence across europe: A study on awareness, attitude and trust. IEEE Transactions on Artificial Intelligence.
  37. Johanne R Trippas, Sara Fahad Dawood Al Lawati, Joel Mackenzie, and Luke Gallagher. 2024. What do users really ask large language models? an initial log analysis of google bard interactions in the wild. In Proceedings of the 47th International ACM SI- GIR Conference on Research and Development in Information Retrieval, pages 2703-2707.
  38. Dennis Ulmer, Christian Hardmeier, and Jes Frellsen. 2022. deep-significance: Easy and meaningful sig- nifcance testing in the age of neural networks. In ML Evaluation Standards Workshop at the Tenth Interna- tional Conference on Learning Representations. UNESCO. 2023. Ai literacy and the new digital divide: A global call to action.
  39. Ziang Xiao, Wesley Hanwen Deng, Michelle S. Lam, Motahhare Eslami, Juho Kim, Mina Lee, and Q. Vera Liao. 2024. Human-centered evaluation and auditing of language models. In Extended Abstracts of the CHI Conference on Human Factors in Computing Systems, CHI EA '24, New York, NY, USA. Associa- tion for Computing Machinery. · DOI
  40. Zhanglei Ye and Jian Li. 2024. Artificial intelligence through the lens of metaphor: Analyzing the eu aia. International Journal of Digital Law and Gover- nance, 1(2):361-381.
  41. Wenting Zhao, Xiang Ren, Jack Hessel, Claire Cardie, Yejin Choi, and Yuntian Deng. 2024. Wildchat: 1m chatgpt interaction logs in the wild. In The Twelfth International Conference on Learning Representa- tions.
  42. Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Tianle Li, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zhuohan Li, Zi Lin, Eric. P Xing, Joseph E. Gonza- lez, Ion Stoica, and Hao Zhang. 2023. Lmsys-chat- 1m: A large-scale real-world llm conversation dataset. Preprint, arXiv:2309.11998. · arXiv

Cited by

Sorted by citations

Authors
Bassignana, E., Curry, A. C., & Hovy, D.
Affiliations
  • IT University of Copenhagen (DK)
  • Bocconi University (IT)
ORCID
Venue
Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Publisher
Association for Computational Linguistics
Published
2025
Pages
18647-18664
ISSN
0736-587X (ISSN-L)
DOI
10.18653/v1/2025.acl-long.914
OpenAlex
W4412944896
Cited by
15
References
1
Citations by year
2026: 12 · 2025: 3
FWCI
11.03
Type
conference paper
Language
en
Open access
Licence
cc-by
Locations
  • Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
  • IT University Of Copenhagen (IT University of Copenhagen) (publishedVersion, other-oa)
  • Research Explorer (The University of Manchester) (submittedVersion)
Funders
  • Villum Fonden
  • European Commission (HORIZON2020)
  • Università Bocconi
  • H2020 European Research Council (949944)
Topics
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
  • Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
Keywords
socioeconomic status, large language models, generative AI, digital divide

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review