Black LLMirror: User (Self) Perceptions in Black American English Interactions with LLMs

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Authors: Mikayla M. Campbell, Joel Mire, Mark Díaz, Maarten Sap
Cited by: 1
References: 52

Abstract

LLMs becoming increasingly personalized to users’ language style raises both excitement and concerns for minority users such as Black American English (BAE) speakers. Yet, previous work has predominantly focused on user perceptions of out-of-context BAE statements by LLMs rather than naturalistic multi-turn interactions, and has ignored such systems’ effects on users’ self-perception. In this work, we examine the effects that multi-turn interactions with speech and text BAE-producing LLMs have on BAE speakers’ perceptions of the LLM and of themselves. We observe a significant change in participant self-esteem following the interactions, and notable qualitative differences between BAE-LLM and Standard American English (SAE) LLM interactions. We also observe significant effects of BAE-usage on user perception of the model within speech-based interactions. Our findings suggest that the effects of BAE-usage by an LLM agent on model- and self-perception among BAE-speaking users are complex and widely varied.

Fields of Study & Subjects

Lexicography and Language Studies Authorship Attribution and Profiling Digital Communication and Language

Publication details

Journal / source
Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems
Publisher
ACM
Pages
1–18
Publication date
13 April 2026