A Strategic Maturity Model for Global AI Governance
Created by Dr. Sharad Maheshwari, imagingsimplified@gmail.com
RATSe Maturity Simulator
Select a maturity level for each pillar to see your overall score. The score is capped by your "weakest link".
Set Pillar Levels
Your Overall Score
FoundationalLevel 1
Your score is capped by your "weakest link". Improve your lowest pillar score to improve your overall score.
Pillar Details & Rubric
Click on any pillar to see the requirements for each maturity level.
The systematic management of risks throughout the AI system's lifecycle with clear accountability and oversight. This pillar measures internal governance and external contributions to societal capacity building.
Level 1: Foundational
Ad-hoc governance on a project-by-project basis. Roles and responsibilities for AI risk are not formally defined or documented.
Level 2: Evolving
A centralized AI governance body is established with a defined charter. A documented risk management process is in place, and voluntary training is available.
Level 3: Advanced
Governance is deeply embedded ("Responsible AI by Design"). Proactive risk management, mandatory role-based training, and proactive regulatory collaboration are standard practice.
Adherence to and verifiable demonstration of compliance with established legal, regulatory, and ethical standards. This pillar measures proactive engagement with regulatory bodies and readiness for new legal frameworks.
Level 1: Foundational
High-level awareness of major regulations (e.g., GDPR, EU AI Act) is present. No formal conformity or impact assessments are conducted.
Level 2: Evolving
A defined, documented compliance program is in place (e.g., as required by the EU AI Act). Regular internal audits are conducted.
Level 3: Advanced
The system has completed formal third-party certification (e.g., ISO/IEC 42001). Proactive engagement with AI Safety Institutes (AISIs) and public channels for user redress are established.
The capacity to provide meaningful, intelligible, and appropriate information about an AI system's function, capabilities, limitations, and decision-making logic to relevant stakeholders.
Level 1: Foundational
Basic system declaration (e.g., "This content is generated by AI"). Internal documentation is limited and not standardized.
Level 2: Evolving
The system can provide a simple, high-level explanation for its specific outputs. A preliminary "Model Card" or "Datasheet" is drafted for internal use.
Level 3: Advanced
The system provides clear, human-intelligible explanations tailored to the stakeholder. Comprehensive, public, and updated Model Cards are maintained, and operations are fully traceable.
The dynamic, socio-technical capability for a system to anticipate, withstand, adapt to, and recover from adverse conditions, including security threats, operational failures, and unexpected inputs.
Level 1: Foundational
Reactive failure handling. Risks are addressed as they arise. No proactive threat modeling or formal incident response plan exists.
Level 2: Evolving
Proactive threat modeling is conducted. The system is tested against known adversarial examples. Basic monitoring for data drift is implemented.
Level 3: Advanced
The system is subjected to regular, formal "red teaming." Continuous, automated monitoring is in place, and a public, documented incident response plan (with MTTR tracking) exists.
This pillar mandates a dual focus: (1) **Procedural Fairness** (the technical mitigation of harmful bias) and (2) **Substantive Equity** (the strategic assessment of systemic disparities and the provision of redress).
Level 1: Foundational
An intention to be fair and avoid bias is stated in project goals. No formal impact assessment or fairness testing is conducted.
Level 2: Evolving
A formal Responsible AI Impact Assessment is conducted. Fairness metrics are tracked at an aggregate level. A mechanism for ad-hoc human oversight exists.
Level 3: Advanced
Includes disaggregated fairness testing, substantive equity assessments to prevent systemic harm, proactive stakeholder engagement, and publicly accessible, no-cost channels for redress and contestability.
The measurement, management, and mitigation of the environmental impacts of the AI system across its entire lifecycle, promoting computational and resource efficiency.
Level 1: Foundational
Project documentation acknowledges the potential energy/carbon impact. A high-level, pre-run estimate of the carbon footprint is generated.
Level 2: Evolving
An open-source tool (e.g., CodeCarbon) is implemented to track and log actual energy consumption (kWh) and operational carbon emissions (CO2eq).
Level 3: Advanced
A comprehensive Lifecycle Assessment (LCA) is produced, including embodied carbon and water usage. Carbon-aware strategies are employed. Metrics are publicly disclosed.
Learn the RATSe Principles
Click on any card to flip it and reveal the answer.
Test Your Knowledge
Check your understanding of the core RATSe concepts with this short quiz.
Global Compliance Mapping
The RATSe framework is designed to be a comprehensive tool that helps you meet the requirements of major global regulations. A high RATSe score provides auditable evidence for the following frameworks:
RATSe Pillar
Mapped Regulations & Standards
Responsible
NIST AI RMF (Govern), EU AI Act (Risk Mgmt), OECD (Accountability), India AI Guidelines (Accountability, People First)
Accountable
EU AI Act (Mandatory Req.), India DPDP Act, NIST AI RMF (Govern), UK (Contestability), India AI (Graded Liability, AISI)
Transparent
EU AI Act (Transparency), NIST AI RMF (Explainability), OECD (Transparency), UK (Explainability), India AI (Understandable by Design)
Safe
EU AI Act (Robustness, Security), NIST AI RMF (Resilience), OECD (Safety & Security), UK (Robustness), India AI (Safety, Resilience)
Ethical & Equitable
EU AI Act (Human Oversight), NIST AI RMF (Fairness), OECD (Human-centric, Fairness), UK (Fairness), India AI (Fairness & Equity, DPI Integration)
Environmental
EU AI Act (Energy Efficiency), NIST AI RMF (Measure 2.12), OECD (Sustainable Dev.), UNESCO (Ecosystem Flourishing), India AI (Sustainability)
Scoring Methodology
The "Non-Linear" & "Weakest Link" Principle
A core feature of the RATSe framework is its **non-linear, "weakest link" scoring model**. This is a deliberate choice to prevent a common flaw in "linear" or "average-based" scoring.
Linear Scoring (The Flawed Way)
A linear model uses an **average**. This is dangerous because it lets high scores hide critical failures.
Example:
5 Pillars at Level 3 (Advanced)
1 Pillar at Level 1 (Foundational)
An average score `(3+3+3+3+3+1) / 6 = 2.66` might be rounded to "Level 3". This falsely claims the system is "Advanced" when it has a critical failure.
Non-Linear / Weakest Link (The RATSe Way)
A non-linear model uses the **minimum** score. It enforces the rule that an AI system is only as trustworthy as its most vulnerable part.
Example:
5 Pillars at Level 3 (Advanced)
1 Pillar at Level 1 (Foundational)
The RATSe score is `MIN(3, 3, 3, 3, 3, 1) = 1`. The system's final score is **Level 1: Foundational**. This correctly reflects the critical risk.
This "weakest link" or "critical failure gateway" model is non-linear because a single low value *overrides* all other high values, rather than just averaging with them. This ensures that to get a high score, you must be mature across **all** pillars.
The Maturity Levels
Level 0: None
No formal processes, documentation, or controls are in place. Work is reactive and compliance is unknown.
Level 1: Foundational
The organization is aware of risks. Basic documentation and controls are in place, but processes are informal, inconsistent, and manually applied.
Level 2: Evolving
Formal, repeatable processes are established and managed. Audits are performed, technical tools are used, and governance is centralized.
Level 3: Advanced
Responsible AI is fully automated and optimized. Principles are embedded in the MLOps pipeline, monitoring is continuous, and a culture of proactive risk management exists.
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