{
  "id": 7099,
  "url": "https://arxiv.org/abs/2603.16827v1",
  "title": "Prompt Programming for Cultural Bias and Alignment of Large Language Models",
  "summary": "Culture shapes reasoning, values, prioritization, and strategic decision-making, yet large language models (LLMs) often exhibit cultural biases that misalign with target populations. As LLMs are increasingly used for strategic decision-making, policy support, and document engineering tasks such as summarization, categorization, and compliance-oriented auditing, improving cultural alignment is important for ensuring that downstream analyses and recommendations reflect target-population value prof",
  "authors": "Maksim Eren, Eric Michalak, Brian Cook, Johnny Seales",
  "category": "research",
  "topics": "bias-fairness,regulation,safety-alignment,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-03-17T17:34:40.000Z",
  "fetched_at": "2026-07-14T16:32:59.164Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/7099",
  "original_url": "https://arxiv.org/abs/2603.16827v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}