Paul Aubrecht (U Passau) and Mitja Kovac (U Ljubljana) have posted “AI Regulation: A Comparison of Centralized and Decentralized Approaches in Federal Systems” on SSRN. Here is the abstract:
The regulation of artificial intelligence (AI) is at the forefront of legal scholarship, as its use has the potential to change nearly every aspect of human society. The potential for AI to impact society makes its regulation particularly relevant to legal scholars, practitioners, and lawmakers.
This research considers the benefits of centralization and decentralization in regulating AI and the implications of convergence or divergence in AI regulation within a federal system. We examine the United States (US) and European Union (EU) approaches to regulation of AI and show that there is a distinct difference in risk preferences related to the regulation of AI between the EU and US as well as a higher likelihood of negative externalities related to the regulation of AI emerging in the US than in the EU while also recognizing that the US approach of decentralized regulation of AI is more likely to lead to the identification of novel approaches to the regulation of AI which may eventually lead to positive externalities. Thus, we generally consider that each approach is not risk-neutral, though for very different reasons.
This examination focuses on the costs and benefits of centralization and decentralization within federal systems, i.e., systems in which regulatory competencies are divided between the federal and state levels. The US and EU provide useful examples of how federal approaches to regulating AI diverge. A quick look at the regulation of AI in the EU and the US shows a divergence in approaches to the regulation of AI within borders, between states inside federal systems (within the US), and across borders between different federal systems (EU and US).