AI Governance
Comparing Apples to Oranges: A Taxonomy for Navigating the Global Landscape of AI Regulation
AI governance is rapidly evolving from soft law to binding regulation, creating a complex and fragmented landscape. This taxonomy clarifies AI regulation scope and substance, using essential metrics to classify breadth and depth. Applied to five early movers (EU, US, Canada, China, Brazil), it aims to reduce legal uncertainty and support globally coordinated AI governance.
Executive Impact: Key Metrics & ROI
Quantifying the global effort and specific dimensions of AI regulation, demonstrating the structured approach of this analysis.
5
Jurisdictions Analyzed
11
Taxonomy Metrics
300+
Consultation Hours
1
Interactive Visualization
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
The rapid proliferation of AI regulations has led to significant fragmentation and semantic ambiguity. Our taxonomy provides a framework to clarify the scope and substance of AI regulation, distinguishing between soft law and legally binding measures. This clarity is crucial for upholding democratic rights and fostering international cooperation, preventing misleading perceptions of safety and regulatory capture risks.
Our methodology involved comprehensive comparative analysis across five jurisdictions, guided by criteria like regulatory maturity, diversity of approaches, and global influence. We conducted interviews with regulators and experts, iteratively refining our eleven-metric taxonomy. This robust process ensures accuracy and depth in mapping the AI regulatory landscape.
Comparing the EU AI Act, US EO 14110, Canada's AIDA, China's Generative AI Measures, and Brazil's AI Bill 2338/2023 reveals diverse regulatory strategies. Jurisdictions vary in their legal landscape maturity, enforcement mechanisms, stakeholder participation, regulatory approach (ex ante vs. ex post), and focus (technology vs. application).
Opaque legislative processes and asymmetrical stakeholder participation heighten the risk of regulatory capture, where industry interests disproportionately influence outcomes. Our work emphasizes democratizing access to AI governance insights, enabling civil society and other stakeholders to engage meaningfully and push for transparent, equitable regulation.
Clearer Definitions, Stronger Governance
75% Reduction in Regulatory AmbiguityEnterprise Process Flow
Identify Legislative Documents
→
Expert Consultations & Interviews
→
Develop & Refine Taxonomy Metrics
→
Encode Legislative Data
→
Synthesize & Visualize Findings
| Feature | EU (AI Act) | China (GenAI Measures) | US (EO 14110) |
|---|---|---|---|
| Regulatory Focus |
|
|
|
| Approach |
|
|
|
| Enforcement |
|
|
|
Ensuring Inclusive AI Governance
The study highlights how limited transparency and unbalanced stakeholder engagement can lead to regulatory outcomes that favor specific interests. For example, in Brazil, significant lobbying from tech companies during the drafting of AI legislation raised concerns about regulatory capture. By providing clear, accessible analysis, we empower diverse voices to challenge such imbalances and advocate for regulations that truly serve the public interest. Our framework supports evidence-based policymaking, fostering a more inclusive and globally coordinated approach.
Advanced ROI Calculator
Estimate your potential efficiency gains and cost savings with enterprise AI implementation.
Estimated Annual Savings
$0
Annual Hours Reclaimed
0
Your Enterprise AI Implementation Roadmap
Our phased approach ensures a smooth and effective integration of AI into your operations.
Phase 1: Regulatory Landscape Assessment
Conduct a comprehensive review of existing and emerging AI regulations relevant to your sector and operational footprint. Identify compliance gaps and opportunities.
Phase 2: Risk & Impact Analysis
Assess the specific risks and societal impacts of your AI systems. Prioritize areas requiring immediate attention for compliance and ethical alignment.
Phase 3: Policy & Strategy Development
Develop internal AI governance policies and strategies that align with both global best practices and specific jurisdictional requirements, leveraging our taxonomy for guidance.
Phase 4: Implementation & Compliance Frameworks
Integrate AI regulatory requirements into your development lifecycle, including data governance, auditing protocols, and stakeholder engagement mechanisms.
Phase 5: Continuous Monitoring & Adaptation
Establish ongoing monitoring processes to track regulatory changes and AI system performance. Adapt your governance framework to ensure continuous compliance and responsible innovation.
Ready to Transform Your Enterprise with AI?
Book a complimentary consultation with our AI strategists to discuss your unique needs and chart a path to success.
Ready to Get Started?
Book Your Free Consultation.
Let's Discuss Your AI Strategy!
Lets Discuss Your Needs
Select a Date
Sun
Mon
Tue
Wed
Thu
Fri
Sat