Between Innovation and Oversight: A Cross-Regional Study of AI Risk Management Frameworks in the EU, U.S., UK, and China
Amir Al-Maamari
TL;DR
This paper provides a cross-regional comparison of AI risk management frameworks in the EU, U.S., UK, and China using a multi-method design (comparative policy analysis, thematic analysis, and case studies). It analyzes risk categorization, governance, transparency, and adaptability, illustrating how each regime addresses high-risk applications in healthcare, autonomous transport, fintech, and public surveillance. Key contributions include a comprehensive framework for evaluating AI governance across regions and policy recommendations for adaptive, inclusive, and globally informed regulation. The findings underscore the need for context-sensitive, globally informed governance that can balance responsible risk management with sustained technological progress.
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.