Document Type

Article

Publication Date

2026

DOI

10.1007/s00146-026-03076-9

Publication Title

AI & Society

Volume

Advance online publication

Pages

1-13

Abstract

How should AI-generated speech balance epistemic aims, such as precision and accuracy, with ethical and social considerations? This paper examines a subtle yet consequential aspect of LLM-driven communication: the use of generic generalizations that convey information about social groups (e.g., “immigrants work low-wage jobs”). While central to human epistemic and pedagogical practices, generics are theorized to reinforce stereotypes, essentialism, and injustice. Using ChatGPT-3.5 as a case study, I uncover tendencies for AI chatbots to inconsistently hedge and refuse generics, including those that reflect well-documented social structural patterns, such as “women are more likely to get attacked while walking alone at night.” These tendencies are not only ethically dubious but also epistemically troubling, since they may reduce the accuracy and informativeness of outputs and impede the communication of important social truths. In response, I highlight the complexity of improving chatbots’ social generic use by evaluating four approaches and show that each approach carries significant tradeoffs. I suggest chatbots should be permitted to use social generics when such generics are paired with clarifications regarding their scope and fungibility. I then offer three complementary strategies to operationalize an Interdisciplinary Expert-Driven Counterfactual Dialogue proposal to improve the accuracy and social responsibility of descriptions of groups and patterns.

Rights

© 2026 The Author.

This article is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original authors and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third-party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Data Availability

Article states: "No datasets were generated or analyzed during the current study."

Original Publication Citation

Zhu, T. A. (2026). How should AI talk about us? LLMs and social generics. AI & Society. Advance online publication. https://doi.org/10.1007/s00146-026-03076-9

ORCID

0009-0008-4195-4659 (Zhu)

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