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Evidence record 10682 · automatically gathered

All too perfect: bias and aspiration in persona generation with LLMs

Synthetic data generated by large language models plays a central role in the training and alignment process of other AI systems. However, this process also risks inheriting the structural biases of organic corpora and embedding new biases that stem from the design choices underlying the data creation process. This paper examines the systematic biases that emerge when large language models (LLMs) are tasked with generating synthetic personas. We introduce a reproducible, minimally conditioned pi

Record details

Published: 15 July 2026
Source: Artificial Intelligence Review
Category: Research
Topics: Bias & fairness · Safety & alignment
Retrieved: 16 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 (15 July 2026), “All too perfect: bias and aspiration in persona generation with LLMs,” evidence record 10682, https://ethics.ai/record/10682 (originally published by Artificial Intelligence Review).

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