An Open Pipeline and Dashboard for Systemic-Risk Evidence under the EU AI Act's Code of Practice

2026-09-23

AI Summary

为了解决AI安全证据不透明问题，研究构建了系统风险指数和开放评估管道及仪表板，通过19个公共基准测试模型的风险，并提供交互式查看功能。

Abstract

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 evidence. Across 18 models, scores fall by 14 to 37 points under worst-case aggregation, highlighting information that can be hidden by an average assessment of model risk. LLM judges show agreement with human graders comparable to human--human agreement ($κ= 0.78\text{--}0.82$), and a blind audit finds that $83\%$ of sampled transformations preserve the original harm. In a survey ($N = 21$), most participants report that scores are easy to understand and that the dashboard encouraged them to view model evaluations under different settings

Problem

• AI safety
• transparent evidence
• systemic risk
• public benchmarks
• interactive dashboard

Innovation

• Systemic Risk Index
• interactive dashboard
• harm-preserving perturbations
• EU GPAI Code of Practice
• transparency