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Reflexive Research with LLMs: Considering the Positionality of Users and Systems

Paper · 2026 · Eleanor L.T. Smith, Luis Morgado da Costa and Antske Fokkens

Summary

This position paper addresses a methodological gap in digital social science and humanities (DSSH): researchers increasingly use LLMs while often treating them as neutral instruments. The authors argue that bias enters from two sides. The first is the researcher, whose interaction with a machine-like or anthropomorphized chat interface can trigger unwarranted trust. The second is the system, whose behaviour reflects its training data and design choices. Drawing on literature from HCI, media studies and NLP, they propose a conceptual model of the factors shaping LLM use in research. The model has three levels: user, system and external (how LLMs are presented). They review evidence on mechanical objectivity, the machine heuristic, shifting mental models, and hype in academic and news discourse. They conclude that established SSH practices should be applied to both researchers and the tools they use. These practices are positionality statements, source criticism and tool criticism. Combined with transparent, case-specific evaluation, they offer a practical lever for more methodologically sound LLM-based research.

Key points

  • The model (Figure 1) separates three kinds of factors:
    • User-level factors: beliefs about capabilities, mental model, and behaviour during interaction.
    • System-level factors: inherent attributes, bias learned from data, and structural bias from design.
    • External factors: commercial, scientific and journalistic presentation, plus the values of the technical community.
  • In the model, upward arrows show that changing user-level factors can influence the wider system of interactions.
  • The paper cites Schneider (2025), who analysed 200,000 human–LLM interactions. Users began adopting human-style conversational behaviour from their second turn onward.
  • In the study by Cabrero-Daniel and Sanagustín Cabrero, about half of the participants considered gender bias intrinsic to text generation rather than an error. This suggests a tendency not to classify such bias as a system fault.
  • Other cited evidence includes the following:
    • Dutch survey respondents rated automated decisions as equal to or better than expert judgments (Araujo et al., 2019).
    • Williams and Huckle's (2024) benchmark shows LLMs failing on tasks that are easy for humans.
    • The GPT-4 technical report acknowledges the model's capacity for confident errors.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Vrije Universiteit AmsterdamAmsterdam · Netherlands42 works · 3,372 citations

 Fields, topics and concepts

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksSocial Sciences · 1,772 worksSafety Research · 543 worksGeneral Social Sciences · 227 worksLiterature and Literary Theory · 519 works

Cite

Cite as Eleanor L.T. Smith, Luis Morgado da Costa and Antske Fokkens. 2026. Reflexive Research with LLMs: Considering the Positionality of Users and Systems. Proceedings of Shaping Multilingual, Multimodal AI for the Social Sciences and Humanities (LLMs4SSH) @ LREC 2026.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.63317/3ycn8grfuwtx

@inproceedings{smith2026reflexive,
  title = {{Reflexive Research with LLMs: Considering the Positionality of Users and Systems}},
  author = {Eleanor L.T. Smith, Luis Morgado {da Costa} and Antske Fokkens},
  booktitle = {Proceedings of Shaping Multilingual, Multimodal AI for the Social Sciences and Humanities (LLMs4SSH) @ LREC 2026},
  year = {2026},
  doi = {10.63317/3ycn8grfuwtx},
  url = {https://doi.org/10.63317/3ycn8grfuwtx}
}

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