AI透明度地图：框架、评分与实时模型卡片评估流程
AI Transparency Atlas: Framework, Scoring, and Real-Time Model Card Evaluation Pipeline
AI总结：
本文提出AI透明度框架和实时评估流程，评估50个模型发现前沿实验室合规率约80%，而多数供应商低于60%，安全关键领域存在显著缺口。
摘要：
AI模型文档在不同平台上碎片化且结构不一致，阻碍了政策制定者、审计员和用户可靠地评估安全声明、数据来源和版本级变化。我们分析了五个前沿模型（Gemini 3、Grok 4.1、Llama 4、GPT-5和Claude 4.5）和100个Hugging Face模型卡片的文档，发现了947个独特的部分名称，具有极端的命名变化。使用信息单独出现在97个不同的标签下。使用欧盟AI法案附录IV和斯坦福透明度指数作为基准，我们开发了一个加权透明度框架，包含8个部分和23个子部分，优先考虑安全关键披露（安全评估：25%，关键风险：20%）而非技术规范。我们实现了一个自动多代理流程，从公共来源提取文档并通过LLM共识评分完整性。在评估50个模型（涵盖视觉、多模态、开源和闭源系统）时，总成本低于3美元，揭示了系统性缺口。前沿实验室（xAI、Microsoft、Anthropic）达到约80%的合规性，而大多数供应商低于60%。安全关键类别显示最大的缺陷：欺骗行为、幻觉和儿童安全评估分别在所有评估模型中导致148、124和116个汇总点损失。
英文摘要：
AI model documentation is fragmented across platforms and inconsistent in structure, preventing policymakers, auditors, and users from reliably assessing safety claims, data provenance, and version-level changes. We analyzed documentation from five frontier models (Gemini 3, Grok 4.1, Llama 4, GPT-5, and Claude 4.5) and 100 Hugging Face model cards, identifying 947 unique section names with extreme naming variation. Usage information alone appeared under 97 distinct labels. Using the EU AI Act Annex IV and the Stanford Transparency Index as baselines, we developed a weighted transparency framework with 8 sections and 23 subsections that prioritizes safety-critical disclosures (Safety Evaluation: 25%, Critical Risk: 20%) over technical specifications. We implemented an automated multi-agent pipeline that extracts documentation from public sources and scores completeness through LLM-based consensus. Evaluating 50 models across vision, multimodal, open-source, and closed-source systems cost less than $3 in total and revealed systematic gaps. Frontier labs (xAI, Microsoft, Anthropic) achieve approximately 80% compliance, while most providers fall below 60%. Safety-critical categories show the largest deficits: deception behaviors, hallucinations, and child safety evaluations account for 148, 124, and 116 aggregate points lost, respectively, across all evaluated models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
