{
  "annotation_id": "26f35128-7e22-4888-a84b-3940c86c560a",
  "date_created": "2026-02-17T05:53:16.244000Z",
  "date_modified": "2026-02-17T05:53:16.244000Z",
  "file_hash": "8620ccdb98a9a11f4a49a8c8ebe348684391e304e6de766279139538998450f6",
  "private": false,
  "record": {
    "abstract": "Large Language Models (LLMs) are increasingly integrated into academic research pipelines; however, the Terms of Service governing their use remain under-examined. We present a comparative analysis of the Terms of Service of five major LLM providers (Anthropic, DeepSeek, Google, OpenAI, and xAI) collected in November 2025. Our analysis reveals substantial variation in the stringency and specificity of usage restrictions for general users and researchers. We identify specific complexities for researchers in security research, computational social sciences, and psychological studies. We identify `regulatory gray areas\u0027 where Terms of Service create uncertainty for legitimate use. We contribute a publicly available resource comparing terms across platforms (OSF) and discuss implications for general users and researchers navigating this evolving landscape.",
    "arxiv_id": "2601.08415",
    "authors": [
      "Brittany I. Davidson",
      "Kate Muir",
      "Florian A. D. Burnat",
      "Adam N. Joinson"
    ],
    "categories": [
      "cs.CY",
      "cs.AI"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "Regulatory gray areas of LLM Terms",
    "url": "https://arxiv.org/abs/2601.08415",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "05ab0486-1ba0-4f50-9a9a-3c3774cd599a",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}