NLP-Based Evaluation of Ethical and Regulatory Frameworks for Artificial Intelligence

Artificial intelligence (AI) is rapidly transforming government operations and influencing social and economic systems across the globe. While AI delivers substantial benefits such as enhanced financial services, improved medical diagnostics, and accelerated decision-making, it also presents critical ethical, legal, and societal challenges. These challenges include algorithmic bias, limited transparency, privacy risks, and unclear accountability for AI-driven outcomes. The long-term success and widespread adoption of AI technologies depend heavily on public trust, which may be undermined by ineffective or ambiguous regulatory frameworks. This study conducts a comparative analysis of AI governance approaches adopted by leading AI-driven regions, including the United States, the European Union, China, and emerging AI hubs. It evaluates regulatory frameworks, ethical standards, and institutional practices using benchmarks such as transparency, fairness, accountability, and stakeholder participation. The analysis is based on legislation, government initiatives, international guidelines, and scholarly literature. The findings indicate that public trust in AI is closely linked to policies that promote openness, enforce accountability, and encourage multi-stakeholder involvement, whereas inconsistent or vague regulations create uncertainty and impede AI diffusion.