arXiv - Cross-Regional AI Risk Management Study
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Useful comparative reference for researchers and policymakers studying international AI governance divergence; provides a structured overview of regulatory philosophies across the four major AI regulatory jurisdictions as of early 2025.
Metadata
Abstract
As artificial intelligence (AI) technologies increasingly enter important sectors like healthcare, transportation, and finance, the development of effective governance frameworks is crucial for dealing with ethical, security, and societal risks. This paper conducts a comparative analysis of AI risk management strategies across the European Union (EU), United States (U.S.), United Kingdom (UK), and China. A multi-method qualitative approach, including comparative policy analysis, thematic analysis, and case studies, investigates how these regions classify AI risks, implement compliance measures, structure oversight, prioritize transparency, and respond to emerging innovations. Examples from high-risk contexts like healthcare diagnostics, autonomous vehicles, fintech, and facial recognition demonstrate the advantages and limitations of different regulatory models. The findings show that the EU implements a structured, risk-based framework that prioritizes transparency and conformity assessments, while the U.S. uses decentralized, sector-specific regulations that promote innovation but may lead to fragmented enforcement. The flexible, sector-specific strategy of the UK facilitates agile responses but may lead to inconsistent coverage across domains. China's centralized directives allow rapid large-scale implementation while constraining public transparency and external oversight. These insights show the necessity for AI regulation that is globally informed yet context-sensitive, aiming to balance effective risk management with technological progress. The paper concludes with policy recommendations and suggestions for future research aimed at enhancing effective, adaptive, and inclusive AI governance globally.
Summary
This comparative policy analysis examines how the EU, U.S., UK, and China each approach AI risk classification, compliance, oversight, and transparency. The study contrasts the EU's structured risk-based regulation, the U.S.'s fragmented sector-specific approach, the UK's agile but inconsistent framework, and China's centralized directives. The authors argue that effective global AI governance requires context-sensitive, internationally-informed regulation that balances risk management with innovation.
Key Points
- •The EU employs a structured, risk-tiered approach (EU AI Act) emphasizing transparency and compliance obligations across AI application categories.
- •The U.S. relies on decentralized, sector-specific regulation that supports innovation but risks regulatory gaps and fragmentation across industries.
- •The UK adopts flexible, principles-based sector guidance enabling agility, though this may produce inconsistent coverage across high-risk AI applications.
- •China's centralized regulatory directives allow rapid nationwide implementation but limit public transparency and independent oversight.
- •Effective global AI governance requires harmonization efforts that are context-sensitive and informed by cross-regional regulatory learnings.
Cited by 1 page
| Page | Type | Quality |
|---|---|---|
| NIST AI Risk Management Framework (AI RMF) | Policy | 60.0 |
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Between Innovation and Oversight: A Cross-Regional Study of AI Risk Management Frameworks in the EU, U.S., UK, and China
Between Innovation and Oversight: A Cross-Regional Study of AI Risk Management Frameworks in the EU, U.S., UK, and China
Amir Al-Maamari \orcidlink 0009-0004-9816-2362
Faculty of Computer Science and Mathematics
University of Passau
Germany, Passau
almaam03@ads.uni-passau.de
Abstract
As artificial intelligence (AI) technologies increasingly enter important sectors like healthcare, transportation, and finance, the development of effective governance frameworks is crucial for dealing with ethical, security, and societal risks. This paper conducts a comparative analysis of AI risk management strategies across the European Union (EU), United States (U.S.), United Kingdom (UK), and China. A multi-method qualitative approach, including comparative policy analysis, thematic analysis, and case studies, investigates how these regions classify AI risks, implement compliance measures, structure oversight, prioritize transparency, and respond to emerging innovations. Examples from high-risk contexts like healthcare diagnostics, autonomous vehicles, fintech, and facial recognition demonstrate the advantages and limitations of different regulatory models.
The findings show that the EU implements a structured, risk-based framework that prioritizes transparency and conformity assessments, while the U.S. uses decentralized, sector-specific regulations that promote innovation but may lead to fragmented enforcement. The flexible, sector-specific strategy of the UK facilitates agile responses but may lead to inconsistent coverage across domains. China’s centralized directives allow rapid large-scale implementation while constraining public transparency and external oversight. These insights show the necessity for AI regulation that is globally informed yet context-sensitive, aiming to balance effective risk management with technological progress. The paper concludes with policy recommendations and suggestions for future research aimed at enhancing effective, adaptive, and inclusive AI governance globally.
K eywords AI Governance, AI Risk Management Frameworks, EU AI Act, Comparative Analysis
1 Introduction
Artificial intelligence (AI) has transitioned from a specialized research domain to a critical technology influencing almost all global industries [ 1 , 2 ] . The applications include healthcare diagnostics, autonomous vehicles, financial analytics, and personalized consumer services [ 3 ] . As AI systems gain prevalence and capability, concerns about their ethical, security, and societal implications have intensified [ 4 ] . Concerns include potential biases in algorithmic decision-making, privacy erosion, and the broader social impacts of automation and surveillance technologies [ 5 , 6 ] .
Policymakers and regulatory bodies have begun to formulate governance strategies
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