Investigators:
Umberto Nizza

Abstract:
The rise of artificial intelligence (AI) has sparked a global debate on ensuring its responsible development and deployment. This paper analyzes the contrasting legal landscapes of the United States (US) and the European Union (EU), employing AI to assess the effectiveness of each system in fostering AI compliance. The analysis adopts a multidisciplinary approach, moving beyond traditional comparisons and challenging dominant narratives about regulatory burdens and innovation through a legal and economic lens.

The US approach resembles a sprawling frontier town, with a patchwork of federal and state-level regulations, executive orders, and industry standards creating a complex and evolving landscape. This fosters regulatory flexibility, allowing companies to experiment and innovate. However, the lack of a centralized framework can make compliance opaque and uneven. AI analysis of these disparate regulations can shed light on potential loopholes and areas where enforcement might be lacking. In contrast, the EU has opted for a more centralized, bureaucratic approach. The landmark EU AI Act establishes a risk-based framework, categorizing AI systems and imposing stricter rules on high-risk applications. This fosters transparency and predictability for businesses, but concerns linger about potential stifling effects on innovation. By analyzing the EU AI Act alongside compliance mechanisms like mandatory risk assessments and human oversight boards, the AI analysis can assess the effectiveness of this approach in achieving its stated goals.

The core of the investigation lies in leveraging AI to compare these contrasting paradigms. AI models can be trained to analyze the language of regulations and industry standards, identifying key compliance requirements and potential areas of ambiguity. Additionally, AI can be used to examine enforcement actions and litigation related to AI, providing insights into how effectively each system is holding companies accountable.

By employing a counterhegemonic lens, the paper avoids simply declaring one approach superior. Instead, it aims to move beyond the tired trope of innovation versus regulation. Through AI-driven analysis, the study can identify strengths and weaknesses within each system, mixing legal and economic tools. The US model might benefit from a more centralized platform consolidating best practices and compliance guidance, while the EU could explore mechanisms to streamline compliance for low-risk applications, fostering continued innovation.

The ultimate goal is not to crown a single winner, but to leverage the power of AI to illuminate a path towards a future where both responsible innovation and robust safeguards for society can co-exist. This comparative analysis, informed by AI, will offer valuable insights for policymakers across the globe as they grapple with the ever-evolving challenge of regulating AI.