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Rethinking Research on Stereotypes: An Analysis through Social Psychological and Computational Perspectives

Paper · 2026 · Kaustubh Shivshankar Shejole and Pushpak Bhattacharyya

Summary

This survey and position paper argues that NLP work on social harms in LLMs often treats bias as a stand-in for stereotypes. The two are distinct concepts, and conflating them has produced inconsistent benchmarks, wasted effort and largely ignored decades of social psychology. The authors pair a review of psychological theory with an audit of how that theory has been used computationally. They cover:

  • seven foundational theories, including social identity, social role, system justification, discursive and intersectionality views;
  • major frameworks such as the Stereotype Content Model and the ABC model;
  • how these ideas have been operationalized so far;
  • computational progress on media narratives, body image, and multilingual, multicultural and multimodal settings.

They then combine both perspectives into one account of open challenges. They treat stereotypes as a root source of downstream harms in responsible AI. They also critique current mitigation methods and propose explainability and interpretability as ways to improve them. The paper offers no new experiments. Its main contribution is a structured research agenda that frames stereotypes in AI as socio-technical phenomena.

Key points

  • The paper separates stereotypes, bias, prejudice and discrimination. It cites MGSD and EMGSD as benchmarks whose inconsistencies come from mixing up stereotypes and bias.
  • Prior work shows that stereotype-aware modeling helps:
    • Tomar et al. (2025a) improved bias detection by adding stereotypes through multi-task learning.
    • Ungless et al. (2022) found that SCM warmth/competence dimensions capture social bias better than general pleasantness terms.
  • The SCM sorts groups into four profiles: admiration, pity, envy and contempt. These profiles are linked to behaviors ranging from active facilitation to active harm.
  • Future directions are collected as a roadmap in Table 3 (Appendix D). They include probing how editing stereotype-related subspaces affects other harms, and making stereotype and bias detectors more robust.
  • The analysis is purely conceptual, and the authors say claims such as stereotypes being a major cause of harm still need empirical testing. The paper appears in Findings of ACL 2026, pp. 1726–1747.

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

🎓Indian Institute of Technology BombayMumbai · India29 works · 2,104 citations

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksSocial Sciences · 1,772 worksSafety Research · 543 worksSociology and Political Science · 577 works

🧭 Topics 1Research subjects and languages the work is about.Bias, fairness & safety · 517 works

Cite

Cite as Kaustubh Shivshankar Shejole and Pushpak Bhattacharyya. 2026. Rethinking Research on Stereotypes: An Analysis through Social Psychological and Computational Perspectives. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.findings-acl.85

@inproceedings{shejole2026rethinking,
  title = {{Rethinking Research on Stereotypes: An Analysis through Social Psychological and Computational Perspectives}},
  author = {Kaustubh Shivshankar Shejole and Pushpak Bhattacharyya},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.85},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.85}
}

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