{
  "id": 5499,
  "url": "https://arxiv.org/abs/2604.20569v1",
  "title": "The Effect of Idea Elaboration on the Automatic Assessment of Idea Originality",
  "summary": "Automatic systems are increasingly used to assess the originality of responses in creative tasks. They offer a potential solution to key limitations of human assessment (cost, fatigue, and subjectivity), but there is preliminary evidence of a self-preference bias. Accordingly, automatic systems tend to prefer outcomes that are more closely related to their style, rather than to the human one. In this paper, we investigated how Large Language Models (LLMs) align with human raters in assessing the",
  "authors": "Umberto Domanti, Moritz Mock, Sergio Agnoli, Antonella De Angeli",
  "category": "research",
  "topics": "bias-fairness,finance-investment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-22T13:49:57.000Z",
  "fetched_at": "2026-07-14T16:31:48.873Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/5499",
  "original_url": "https://arxiv.org/abs/2604.20569v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}