The Precautionary Principle and the Innovation Principle: Incompatible Guides for AI Innovation Governance?

TL;DR

This paper analyzes the compatibility of the Precautionary and Innovation Principles in AI governance, proposing a signal detection framework that suggests they can be aligned through structured testing and adaptive policies.

Contribution

It introduces a signal detection theory model to reconcile weak forms of PP and IP, and discusses how sandboxing can facilitate adaptive AI regulation.

Findings

Weak-PP red-light and weak-IP green-light are optimal under specific error cost ratios.

Sandboxing enables learning and adaptation to maintain optimal regulation.

Foundation models are unsuitable for sandboxing due to their general-purpose nature.

Abstract

In policy debates concerning the governance and regulation of Artificial Intelligence (AI), both the Precautionary Principle (PP) and the Innovation Principle (IP) are advocated by their respective interest groups. Do these principles offer wholly incompatible and contradictory guidance? Does one necessarily negate the other? I argue here that provided attention is restricted to weak-form PP and IP, the answer to both of these questions is "No." The essence of these weak formulations is the requirement to fully account for type-I error costs arising from erroneously preventing the innovation's diffusion through society (i.e. mistaken regulatory red-lighting) as well as the type-II error costs arising from erroneously allowing the innovation to diffuse through society (i.e. mistaken regulatory green-lighting). Within the Signal Detection Theory (SDT) model developed here, weak-PP red-light (weak-IP green-light) determinations are optimal for sufficiently small (large) ratios of expected type-I to type-II error costs. For intermediate expected cost ratios, an amber-light 'wait-and-monitor' policy is optimal. Regulatory sandbox instruments allow AI testing and experimentation to take place within a structured environment of limited duration and societal scale, whereby the expected cost ratio falls within the 'wait-and-monitor' range. Through sandboxing regulators and innovating firms learn more about the expected cost ratio, and what respective adaptations -- of regulation, of technical solution, of business model, or combination thereof, if any -- are needed to keep the ratio out of the weak-PP red-light zone. Nevertheless AI foundation models are ill-suited for regulatory sandboxing as their general-purpose nature precludes credible identification of misclassification costs.