Nirvana AI Governance: How AI Policymaking Is Committing Three Old Fallacies

TL;DR

This paper critiques common fallacies in AI governance proposals by applying the nirvana approach, revealing flaws in assumptions about regulation, risks, and idealized worlds, thereby urging more realistic policymaking.

Contribution

It introduces the nirvana approach to analyze AI policy fallacies, exposing fundamental flaws in current regulatory proposals and emphasizing the importance of realistic comparisons.

Findings

Identifies three common fallacies in AI governance proposals.

Shows that regulatory tools often overlook inherent harms and costs.

Highlights the risks of idealizing solutions without real-world considerations.

Abstract

This research applies Harold Demsetz's concept of the nirvana approach to the realm of AI governance and debunks three common fallacies in various AI policy proposals--"the grass is always greener on the other side," "free lunch," and "the people could be different." Through this, I expose fundamental flaws in the current AI regulatory proposal. First, some commentators intuitively believe that people are more reliable than machines and that government works better in risk control than companies' self-regulation, but they do not fully compare the differences between the status quo and the proposed replacements. Second, when proposing some regulatory tools, some policymakers and researchers do not realize and even gloss over the fact that harms and costs are also inherent in their proposals. Third, some policy proposals are initiated based on a false comparison between the AI-driven world, where AI does lead to some risks, and an entirely idealized world, where no risk exists at all. However, the appropriate approach is to compare the world where AI causes risks to the real world where risks are everywhere, but people can live well with these risks. The prevalence of these fallacies in AI governance underscores a broader issue: the tendency to idealize potential solutions without fully considering their real-world implications. This idealization can lead to regulatory proposals that are not only impractical but potentially harmful to innovation and societal progress.