{
  "@context": {
    "dcterms": "http://purl.org/dc/terms/",
    "dcmitype": "http://purl.org/dc/dcmitype/"
  },
  "dcterms:identifier": [
    "tag:aim-pro.eu,2026:oer/5071ae530e53",
    "https://arxiv.org/abs/2505.07178",
    "arxiv:2505.07178"
  ],
  "dcterms:title": [
    "Accountability of Generative AI: Exploring a Precautionary Approach for \"Artificially Created Nature\""
  ],
  "dcterms:type": [
    {
      "@id": "dcmitype:Text"
    },
    "preprint"
  ],
  "dcterms:creator": [
    "Yuri Nakao"
  ],
  "dcterms:description": [
    "The rapid development of generative artificial intelligence (AI) technologies raises concerns about the accountability of sociotechnical systems. Current generative AI systems rely on complex mechanisms that make it difficult for even experts to fully trace the reasons behind the outputs. This paper first examines existing research on AI transparency and accountability and argues that transparency is not a sufficient condition for accountability but can contribute to its improvement. We then discuss that if it is not possible to make generative AI transparent, generative AI technology becomes ``artificially created nature'' in a metaphorical sense, and suggest using the precautionary principle approach to consider AI risks. Finally, we propose that a platform for citizen participation is needed to address the risks of generative AI."
  ],
  "dcterms:subject": [
    "cs.AI (arxiv)"
  ],
  "dcterms:language": [
    "en"
  ],
  "dcterms:license": [
    {
      "@id": "http://creativecommons.org/licenses/by/4.0/"
    }
  ],
  "dcterms:rights": [
    "CC-BY-4.0 — assessed as OPEN by the harvester's licence gate. Conditions: attribution required. open, permissive"
  ],
  "dcterms:accessRights": [
    "open"
  ],
  "dcterms:format": [
    "text/markdown"
  ],
  "dcterms:extent": [
    "18922 bytes (extracted text)"
  ],
  "dcterms:issued": [
    "2025-05-12"
  ],
  "dcterms:provenance": [
    "Retrieved from arXiv on 2026-10-09 in response to the search string “(all:\"artificial intelligence\" OR all:\"machine learning\" OR all:\"generative AI\" OR all:\"deep learning\" OR all:\"reinforcement learning\" OR all:\"large language model\") AND (all:\"AI concepts\" OR all:\"types of AI\" OR all:\"AI fundamentals\" OR all:\"recognizing AI\" OR all:\"recognising AI\" OR all:\"general versus narrow AI\" OR all:\"narrow AI\" OR all:\"general AI\" OR all:\"machine intelligence\" OR all:\"AI strengths and weaknesses\" OR all:\"traditional software\" OR all:\"rule-based systems\" OR all:\"introduction to AI\" OR all:\"introduction to artificial intelligence\" OR all:\"artificial intelligence introduction\" OR all:\"AI primer\" OR all:\"foundations of artificial intelligence\" OR all:\"overview of AI\" OR all:\"understanding AI\" OR all:\"history of AI\" OR all:\"AI essentials\" OR all:\"AI terminology\" OR all:\"metaphors for AI\" OR all:\"AI fundamental concepts\" OR all:\"AI key concepts\" OR all:\"philosophy of AI\" OR all:\"critical AI literacy\")”. arXiv served the resource and is not asserted to be its publisher or author.",
    "Text extracted from latexml-html to Markdown by arxiv-html; the original is retained unchanged beside it."
  ]
}