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MilaNLProc/survey-language-technologies

The AI Gap: How Socioeconomic Status Affects Language Technology Interactions πŸ† Best Social Impact Paper Award at ACL 2025 Dataset Summary This dataset comprises responses from 1,000 individuals from diverse socioeconomic backgrounds, collected to study how socioeconomic status (SES) influences interaction with language technologies, particularly generative AI and large language models (LLMs). Participants shared demographic and socioeconomic data, as well as… See the full description on the dataset page: https://huggingface.co/datasets/MilaNLProc/survey-language-technologies.

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The AI Gap: How Socioeconomic Status Affects Language Technology Interactions

πŸ† Best Social Impact Paper Award at ACL 2025

Dataset Summary

This dataset comprises responses from 1,000 individuals from diverse socioeconomic backgrounds, collected to study how socioeconomic status (SES) influences interaction with language technologies, particularly generative AI and large language models (LLMs). Participants shared demographic and socioeconomic data, as well as up to 10 real prompts they previously submitted to LLMs like ChatGPT, totaling 6,482 unique prompts.


Dataset Structure

The dataset is provided as a single CSV file:

survey_language_technologies.csv
Column NameDescription
idAnonymized respondent ID
genderGender identity (Male, Female, Non-binary, Other, Prefer not to say)
gender_otherCustom gender identity if "Other" was selected
ageAge group (e.g., 18–24, 25–34, etc.)
nationalityOne or more nationalities (semicolon-separated)
ethnicityOne or more ethnic identities (semicolon-separated)
ethnicity_otherCustom ethnicity if "Other" was selected
maritalMarital status
marital_otherCustom marital status if "Other" was selected
languageFirst language(s) (semicolon-separated)
language_otherCustom language if "Other" was selected
religionReligious affiliation
religion_otherCustom religion if "Other" was selected
educationParticipant’s highest education level
mum_educationMother's highest education level
dad_educationFather's highest education level
sesSelf-assessed SES on a 1–10 ladder scale
homeHome ownership status (Own, Rent, Other)
home_otherCustom home ownership type if "Other" was selected
employmentCurrent employment status
occupationCurrent or most recent job (semicolon-separated if multiple)
mother_occupationMother's occupation(s)
father_occupationFather's occupation(s)
hobbiesHobbies and free-time activities (semicolon-separated)
hobbies_otherCustom hobbies if "Other" was selected
techDaily-used digital devices (semicolon-separated)
tech_otherCustom digital devices if "Other" was selected
know_nlpNLP tools the user is familiar with (semicolon-separated)
know_nlp_otherCustom tools if "Other" was selected
use_nlpNLP tools the user has used (semicolon-separated)
use_nlp_otherCustom tools if "Other" was selected
would_nlpNLP tools find useful but not used because of scance performance (semicolon-separated)
would_nlp_otherCustom tools if "Other" was selected
frequency_llmFrequency of LLM use (Every day, Nearly every day, Sometimes, Rarely, Never)
llm_useLLMs used (e.g., ChatGPT, Claude, Bard, etc.)
llm_otherCustom LLMs if "Other" was selected
usecasesTasks performed with LLMs (e.g., Writing, Learning, Coding, etc.)
usecases_otherCustom tasks if "Other" was selected
contextsContexts in which LLMs are used (e.g., Work, Personal, School)
contexts_otherCustom context if "Other" was selected
prompt1–prompt10Up to 10 prompts submitted by the participant to any AI chatbot
commentsOpen-ended user comments
Note: All multi-select fields are semicolon (;) separated.

Citation

If you use this dataset in your research, please cite the associated paper:

@inproceedings{bassignana-etal-2025-ai,
    title = "The {AI} Gap: How Socioeconomic Status Affects Language Technology Interactions",
    author = "Bassignana, Elisa  and
      Curry, Amanda Cercas  and
      Hovy, Dirk",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.acl-long.914/",
    pages = "18647--18664",
    ISBN = "979-8-89176-251-0",
    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 entail 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 create a 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."
}

Dataset Curators

  • β€”Elisa Bassignana (IT University of Copenhagen)
  • β€”Amanda Cercas Curry (CENTAI Institute)
  • β€”Dirk Hovy (Bocconi University)

Links

  • β€”πŸ“‚ Dataset file: survey_language_technologies.csv
  • β€”πŸ“„ Survey Interface (may take some time to load): https://nlp-use-survey.streamlit.app/
  • β€”πŸ“ Paper: https://aclanthology.org/2025.acl-long.914/