Forensic Policy Benchmarking
Methodology & Design

1. Introduction to the Scoring Index

The Atlas AI Governance Maturity Index represents a rigorous, indicator-based comparative analysis model developed by AI Governance Researcher Nafiul Ahmad Rafi. Rather than performing qualitative approximations, the index utilizes a systematic natural language evaluation and indicator mapping model. It is designed to evaluate policy documents, national strategies, and drafted AI regulations on the presence, density, and strength of core governance provisions.

2. The 6-Pillar Structural Framework

Every submitted policy document is systematically parsed across 6 structural governance pillars. Each pillar is mapped to specific keywords representing critical institutional indicators:

Pillar	Primary Keywords / Indicators Mapped	OECD Framework	EU AI Act Equivalent
Transparency & Explainability	transparent, transparency, explainability, interpretability, audit, disclosure, open data, XAI	Principle 1.3	Article 13
Risk Management & Accountability	risk, liability, accountability, conformity assessment, incident logging, redress mechanism, responsible AI	Principle 1.5	Article 9 & 43
Human Agency & Oversight	human oversight, human-in-the-loop (HITL), meaningful control, override, human review, human-centric	Principle 1.4	Article 14
Safety, Security & Robustness	safety, security, secure, robust, cybersecurity, adversarial testing, integrity, stress-testing	Principle 1.4	Article 15
Socio-economic Well-being	inclusion, equity, gender, sustainability, environment, labor, digital divide, vulnerable, social welfare	Principle 1.2	Recital 6 / FRIA
Innovation & Research Support	innovation, research, r&d, sandbox, startup, funding, talent, capacity building, compute infrastructure	Principle 2.1	Articles 57 - 63

3. Algorithmic Formulation & Math Model

The overall score and the individual pillar percentages are calculated through a multi-stage deterministic math model:

A. Individual Pillar Score Calculation

The indicator coverage for each individual pillar ($S_p$) is calculated based on the ratio of active governance keywords found in the submitted text to the total reference keywords defined for that pillar in the database:

B. Overall Maturity Index Score

The overall index score ($M_{index}$) is derived by calculating the mean of the pillar coverages, and then applying a **distribution density modifier** (calibrated at 1.8) to account for structural gaps. The score is mathematically capped at 98/100 to reflect that no policy is perfectly future-proof:

This formulation ensures that a country policy must have substantial coverage across all six pillars to achieve an advanced rating. A document that scores 100% on one pillar but completely ignores the other five will receive a low overall index rating due to the structural distribution penalty.

4. Framework Alignment Checklist

Beyond pillar scores, the tool performs a direct compliance cross-reference against international frameworks. An indicator is considered **Passed** if the corresponding pillar coverage exceeds 45%, **Partial** if it falls between 20% and 45%, and **Failed** if it falls below 20%:

• OECD AI Principles (2024): Primary focus on Principle 1.3 (Transparency), 1.5 (Risk), 1.2 (Socio-economic), 1.4 (Safety), and 2.1 (Innovation).
• UNESCO Ethics of AI (2021): Primary focus on Article 4 (Transparency), Art. 7 (Human oversight), Art. 5 (Safety), Art. 12 (Socio-economic), and Art. 15 (Research).
• EU AI Act (2024): Strictly mapped to Article 13 (Transparency), Art. 9 (Risk management), Art. 14 (Human oversight), Art. 15 (Robustness), and Art. 57 (Regulatory sandboxes).
• NIST AI RMF 1.0: Cross-referenced with GOVERN 1.2 (Transparency), MANAGE (Risk), MAP 1.5 (Socio-economic), MEASURE 2 (Safety), and GOVERN 6 (Human agency).

5. Global South & LDC Analytical Lens

A core element of Nafiul Ahmad Rafi's research is the **Developing Country & LDC Policy Lens**. Most national AI policies in developing economies are copied directly from Global North frameworks without considering structural differences. The Atlas AI Institute methodology integrates a critical evaluation of these factors:

• Regulatory Capacity: Evaluates if the policy establishes independent regulatory agencies or places unrealistic burdens on existing, under-resourced public offices.
• Compute Sovereignty: Analyzes if the policy contains plans to build national compute capabilities or relies entirely on foreign cloud providers.
• Informal Economies: Assesses whether the policy accounts for the fact that a vast majority of the population works in informal sectors where high-tech labor laws and automated hiring tools are hard to regulate.
• Data Colonialism: Flags strategies that allow unilateral export of sovereign citizen data without domestic hosting or local value creation.