{
  "id": 10682,
  "url": "https://link.springer.com/article/10.1007/s10462-026-11641-3",
  "title": "All too perfect: bias and aspiration in persona generation with LLMs",
  "summary": "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",
  "authors": null,
  "category": "research",
  "topics": "bias-fairness,safety-alignment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-15T00:00:00.000Z",
  "fetched_at": "2026-07-16T05:10:56.605Z",
  "source_slug": "x-artificial-intelligence-review",
  "source_name": "Artificial Intelligence Review",
  "source_homepage": "https://link.springer.com/journal/10462",
  "ethics_ai_record_url": "https://ethics.ai/record/10682",
  "original_url": "https://link.springer.com/article/10.1007/s10462-026-11641-3",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}