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Evidence record 7017 · automatically gathered

Analysis Of Linguistic Stereotypes in Single and Multi-Agent Generative AI Architectures

Many works in the literature show that LLM outputs exhibit discriminatory behaviour, triggering stereotype-based inferences based on the dialect in which the inputs are written. This bias has been shown to be particularly pronounced when the same inputs are provided to LLMs in Standard American English (SAE) and African-American English (AAE). In this paper, we replicate existing analyses of dialect-sensitive stereotype generation in LLM outputs and investigate the effects of mitigation strategi

Record details

Published: 19 March 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Agents & autonomy · Finance, VC & PE
Retrieved: 14 July 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.

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How to cite this record

ethics.ai (19 March 2026), “Analysis Of Linguistic Stereotypes in Single and Multi-Agent Generative AI Architectures,” evidence record 7017, https://ethics.ai/record/7017 (originally published by arXiv).

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