# Between Innovation and Oversight: A Cross-Regional Study of AI Risk Management Frameworks in the EU, U.S., UK, and China

arXiv: https://arxiv.org/abs/2503.05773 · HTML: https://mlsanity.com/paper/2503.05773/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 99 references that are in the map.
- First posted on arXiv 25 Feb 2025.

## Builds on (most cited first, 6 of 6)

- [Risk management in the Artificial Intelligence Act](https://mlsanity.com/paper/2212.03109/) (2022)
- [A Comprehensive Review and Systematic Analysis of Artificial Intelligence Regulation Policies](https://mlsanity.com/paper/2307.12218/) (2023)
- [Responsible AI Governance: A Systematic Literature Review](https://mlsanity.com/paper/2401.10896/) (2023)
- [Navigating Governance Paradigms: A Cross-Regional Comparative Study of Generative AI Governance Processes & Principles](https://mlsanity.com/paper/2408.16771/) (2024)
- [Information That Matters: Exploring Information Needs of People Affected by Algorithmic Decisions](https://mlsanity.com/paper/2401.13324/) (2024)
- [Testing autonomous vehicles and AI: perspectives and challenges from cybersecurity, transparency, robustness and fairness](https://mlsanity.com/paper/2403.14641/) (2024)

## Built on by (most cited first, 2 of 2)

- [Overview of Risk Assessment and Management for Intelligent Systems under the AI Act and Beyond](https://mlsanity.com/paper/2607.02197/) (2026)
- [The Challenges of Balancing AI Compliance and Technological Innovations in Critical Sectors: A Systematic Literature Review](https://mlsanity.com/paper/2606.12423/) (2026)

## Abstract (arXiv)

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.