全球责任AI指数2026版构建五维评估框架,量化各国AI治理水平。
Global Index on Responsible AI 2026 : Conceptual Framework and Methodology
- 五维评估:包容性、伦理可持续性、劳工技能、信任安全、公共领域用AI
- 38项指标分三支柱,政策占比60%、公民参与10%、支撑条件30%
- 通过135国调研+数据审计,公开评分并设不可接受风险使用惩罚
本报告介绍《全球责任人工智能指数》(GIRAI)第二版的方法论。相较于首版,本版强化了框架存在与实施之间的区分,将维度从三类重构为五类主题领域,引入更细粒度的框架质量变量,并采用多阶段评审与验证流程。独立统计预审计评估了框架的一致性与稳健性。GIRAI从五个维度评估负责任的人工智能治理:包容与多样性、伦理与可持续性、劳动与技能、信任与安全,以及人工智能在公共服务中的应用。每个维度包含多项指标(共38项),分为三大支柱:人工智能政策(17项,基于原始数据)、公民社会组织参与(5项,原始数据)、赋能条件(15项,基于二次数据)。此外,单独设立政府使用不可接受风险人工智能(URAI)的指标(原始数据),作为最终得分的扣减项。数据由135名国家层面的研究人员通过结构化全球调查收集,辅以二次数据集。各项数据点的计数、范围、可执行性、主题覆盖和实施程度被编码为数值变量,归一化至100分制,按支柱权重加权聚合。该文档支持跨国系统比较,助力政策制定者、民间社会及人工智能开发者识别承诺转化为可执行保护的环节与关键缺口。
原文摘要 · Abstract (English)
This report presents the methodology of the Global Index on Responsible AI (GIRAI), 2nd Edition. This edition refines the 1st Edition by strengthening the distinction between framework existence and implementation, restructuring dimensions from three to five thematic areas, introducing more granular variables for framework quality, and applying a multi-stage review and validation process. An independent statistical pre-audit was conducted to assess the coherence and robustness of the framework. GIRAI assesses responsible AI governance across five dimensions: Inclusion and Diversity, Ethics and Sustainability, Labour and Skills, Trust and Safety, and Use of AI in Public Service. Each dimension has a number of indicators (38 in total), organised into three pillars, namely AI Policy (17 indicators on government frameworks and implementation, assessed through primary data), CSO Engagement (5 indicators, primary data), and Enabling Conditions (15 indicators on the structural factors shaping responsible AI governance, assessed through secondary data), and a government Use of Unacceptable Risk AI (URAI) indicator (primary data), applied separately as an accountability penalty to the final score. Data was collected by 135 country-level researchers through a structured global survey, complemented by secondary datasets. The count, scope, enforceability, thematic coverage, and implementation levels of the data points are coded into numerical variables, normalised to a scale of 100, aggregated through pillar weights of 60% (AI policy), 10% (CSO Engagement), and 30% (Enabling conditions). A deduction penalty is applied for countries with evidence of URAI. This documentation enables systematic cross-national comparison, supporting policymakers, civil society, and AI developers to identify where commitments are translating into enforceable protections and where critical gaps remain.
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