Global Index on Responsible AI 2026 : Conceptual Framework and Methodology

• URL: http://arxiv.org/abs/2608.18122v1
• Date: Thu, 16 Jul 2026 11:26:21 GMT
• Title: Global Index on Responsible AI 2026 : Conceptual Framework and Methodology
• Abstract summary: Report presents the methodology of the Global Index on Responsible AI (GIRAI), 2nd Edition.<n>This edition refines the 1st Edition by strengthening the distinction between framework existence and implementation.<n>An independent statistical pre-audit was conducted to assess the coherence and robustness of the framework.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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