Template-Type: ReDIF-Paper 1.0
Author-Name: Kim Kaivanto
Author-Name-First: Kim
Author-Name-Last: Kaivanto
Title: The Precautionary Principle and the Innovation Principle
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 redlighting) 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.
Creation-Date: 2025
File-URL: http://www.lancaster.ac.uk/media/lancaster-university/content-assets/documents/lums/economics/working-papers/LancasterWP2025_003.pdf
File-Format: application/pdf
Number: 423283411
Classification-JEL: D81, O31, O33, O38
Keywords: artificial intelligence, foundational AI, general-purpose AI systems, AI governance, precautionary principle, innovation principle, countervailing risk, scientific uncertainty, signal detection theory, misclassification costs, discriminability, ROC curve, de minimis risk, trust and polarization, protected values, non-comparable values, continuity axiom, regulatory sandboxes
Handle: RePEc:lan:wpaper:423283411