2026年全球负责任AI指数：概念框架与方法论
Global Index on Responsible AI 2026 : Conceptual Framework and Methodology
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中文总结 AI 辅助
该研究介绍2026年全球负责任AI指数（GIRAI）第二版的方法论，优化框架维度与指标，经审计验证，用于跨国评估各国负责任AI治理，助力相关主体识别保护成效与差距。
中文摘要 AI 辅助
本报告介绍了第二版全球负责任AI指数（Global Index on Responsible AI，GIRAI）的方法论。该版本对第一版进行了优化，强化了框架存在性与实施情况的区分，将维度从3个重组为5个主题领域，引入了更细化的框架质量变量，并采用了多阶段审查与验证流程。为评估框架的一致性与稳健性，开展了独立的统计预审计。GIRAI从5个维度评估负责任AI治理：包容性与多样性、伦理与可持续性、劳工与技能、信任与安全，以及公共服务中AI的使用。每个维度包含若干指标（共38个），分为三大支柱：AI政策（17个关于政府框架与实施的指标，通过原始数据评估）、民间社会组织（CSO）参与度（5个指标，原始数据）、赋能条件（15个关于塑造负责任AI治理的结构性因素的指标，通过二手数据评估），以及政府使用不可接受风险AI（Unacceptable Risk AI，URAI）指标（原始数据），该指标单独作为问责罚则应用于最终得分。数据由135名国家级研究人员通过结构化全球调查收集，并辅以二手数据集。对数据点的数量、范围、可执行性、主题覆盖范围及实施水平进行编码，转化为数值变量，标准化为100分制，通过三大支柱权重汇总：AI政策占60%、CSO参与度占10%、赋能条件占30%。对有URAI证据的国家应用扣分罚则。本文件支持系统的跨国比较，助力政策制定者、民间社会及AI开发者识别承诺转化为可执行保护的领域，以及存在的关键差距。
英文摘要
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.
