Balancing the tradeoff between regulation and innovation for artificial intelligence: An analysis of top-down command and control and bottom-up self-regulatory approaches

Abstract

In response to the rapid development of AI, several governments have established a variety of regulatory interventions for this technology. While some countries prioritize consumer protection through stringent regulation, others promote innovation by adopting a more hands-off approach. However, this tradeoff has not been analyzed systematically. We developed an economic theory on how the welfare-maximizing level of regulatory stringency for AI depends on various institutional parameters. Our game-theoretic model is motivated and built upon the comparison of regulatory documents for AI from the EU, the UK, the US, Russia, and China. The results show that if a government strives to find the right balance between innovation and consumer protection to maximize actual consumer welfare, stringent regulation is optimal when foreign competition is either high or low, whereas light-touch regulation is optimal when foreign competition is intermediate. Meanwhile, minimal regulation is rationalizable only if a government prioritizes other objectives in its agenda, such as maximizing innovation, domestic producer surplus, or perceived consumer welfare.