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论文精选 65arXiv

An Open Pipeline and Dashboard for Systemic-Risk Evidence under the EU AI Act's Code of Practice· 欧盟 AI 法规下的系统性风险证据公开管道和仪表盘

Claims about AI safety reach audiences well beyond the AI community, yet many rely on opaque evidence or static assessments, when supporting evidence is accessible at all. We present the Systemic Risk Index, an open evaluation pipeline and dashboard built to make empirical evidence more transparent and traceable to the public. Our work organizes 19 public benchmarks into four systemic-risk categories defined by the EU GPAI Code of Practice---CBRN, cyber offense, harmful manipulation, and loss of control---and evaluates models using harm-preserving perturbations and simulated deployment contexts. The interactive dashboard lets users alternate between average and worst-case aggregation, vary how model capability affects the aggregate score, and trace each risk rating to its benchmark evidenc

AI 解读论文

公开 AI 安全基准测试和系统性风险评估结果的管道与仪表盘

核心方法
将 19 个公开基准测试组织成 4 类系统性风险,使用保持危害性的扰动和模拟部署情境评估模型,并通过交互式仪表盘呈现结果
适合谁读
研究者、政策制定者、公众
要解决的问题
当前关于 AI 安全的主张往往依赖不透明或静态的证据,缺乏对公众的透明度和可追溯性
关键实验
组织了 19 个公开基准测试,评估了多种模型在不同风险类别下的表现
主要贡献
提高了 AI 安全证据的透明度和可追溯性,支持公众更深入了解系统性风险
意义与局限
为 AI 安全提供了动态且透明的评估工具,有助于制定更科学的监管政策;但其覆盖的风险类别可能不全面
领域:cs.AI作者:Jacob T. Emmerson、Phuong-Anh Nguyen-Le、Ronan Romano
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论文精选 65已解读

Simulated Control Evals· 模拟控制评估

Control evaluations measure whether safeguards can prevent an AI model from causing undesi…

领域:cs.AI作者:Jeffrey Olmo、David Zhang、Tyler Tracy
📎 arXiv🕒 10-23 01:26🔗 arxiv.org

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