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Evidence record 19446 · automatically gathered

It's How You Ask: Gender-Associated Linguistic Bias in LLMs

Professional communication is increasingly mediated by LLMs - but do these models serve all users equally? We show that when prompts contain linguistic features more commonly used by women (hedges, tag questions, collective reference), they systematically elicit shorter, less sophisticated, and less formal responses across three document types and four models. These effects persist after controlling for prompt complexity and feature carry-over. Explicit gender cues like sign-off names are encode

Record details

Published: 13 August 2026
Source: arXiv cs.AI
Category: Research
Topics: Bias & fairness
Retrieved: 14 August 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

Shared topic: Bias & fairness

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Shared topic: Bias & fairness

Reassessing demographic bias in face attribute classification: a statistically grounded multi-model evaluation on FairFace and UTKFace

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Shared topic: Bias & fairness

Language Models as a Challenge for Business Ethics – A Partially Open-Source Approach

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How to cite this record

ethics.ai (13 August 2026), “It's How You Ask: Gender-Associated Linguistic Bias in LLMs,” evidence record 19446, https://ethics.ai/record/19446 (originally published by arXiv cs.AI).

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