PRISM风险信号框架：基于层级的AI行为风险红线
PRISM Risk Signal Framework: Hierarchy-Based Red Lines for AI Behavioral Risk
AI总结：
本文提出基于层级的PRISM框架，定义27种行为风险信号，通过双重阈值原则评估，区分确认风险与预警信号，提升AI安全性的前瞻性、全面性和可测量性。
摘要：
当前AI安全方法在案例层面定义红线：特定提示、输出或危害。本文主张在价值、证据和来源层级上设定更根本的红线。基于PRISM框架，定义了27种行为风险信号，通过绝对排名位置和相对胜率差距的双重阈值原则，产生两级分类（确认风险 vs. 预警信号）。层级方法相比案例特定红线具有三个优势：前瞻性（在产生有害输出前检测危险推理结构）、全面性（单一价值层级信号涵盖无限案例违反）和可测量性（基于经验强制选择数据）。通过约397,000个强制选择响应，展示框架检测能力，区分结构极端、情境依赖风险和平衡层级模型。
英文摘要：
Current approaches to AI safety define red lines at the case level: specific prompts, specific outputs, specific harms. This paper argues that red lines can be set more fundamentally -- at the level of value, evidence, and source hierarchies that govern AI reasoning. Using the PRISM (Profile-based Reasoning Integrity Stack Measurement) framework, we define a taxonomy of 27 behavioral risk signals derived from structural anomalies in how AI systems prioritize values (L4), weight evidence types (L3), and trust information sources (L2). Each signal is evaluated through a dual-threshold principle combining absolute rank position and relative win-rate gap, producing a two-tier classification (Confirmed Risk vs. Watch Signal). The hierarchy-based approach offers three advantages over case-specific red lines: it is anticipatory rather than reactive (detecting dangerous reasoning structures before they produce harmful outputs), comprehensive rather than enumerative (a single value-hierarchy signal subsumes an unlimited number of case-specific violations), and measurable rather than subjective (grounded in empirical forced-choice data). We demonstrate the framework's detection capacity using approximately 397,000 forced-choice responses from 7 AI models across three Authority Stack layers, showing that the signal taxonomy successfully discriminates between models with structurally extreme profiles, models with context-dependent risk, and models with balanced hierarchies.
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。
